---
title: "Probing the Plasticity and Correlation of LLM Value Systems:"
description: "The authors build on the LITMUSVALUES setup. 153 references. Earlier work found that different LLMs rank their values in similar ways."
authors: ["Zhenheng Tang", "Qihua Pan", "Jingya Shen", "Xiang Liu", "Qian Wang", "Bo Li", "Xiaowen Chu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1715/"
doi: "10.18653/v1/2026.findings-acl.1715"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tang-2026-probing-plasticity-correlation-llm-value/"
---
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# Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable

**Authors:** Zhenheng Tang, Qihua Pan, Jingya Shen, [Xiang Liu](https://md.research.elamur.ai/free/en/authors/xiang-liu/), [Qian Wang](https://md.research.elamur.ai/free/en/authors/qian-wang/), [Bo Li](https://md.research.elamur.ai/free/en/authors/bo-li/) and [Xiaowen Chu](https://md.research.elamur.ai/free/en/authors/xiaowen-chu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1715/) · [PDF](https://aclanthology.org/2026.findings-acl.1715.pdf) · [DOI 10.18653/v1/2026.findings-acl.1715](https://doi.org/10.18653/v1/2026.findings-acl.1715) · [OpenAlex](https://openalex.org/W7166886937)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Earlier work found that different LLMs rank their values in similar ways. This paper asks whether those rankings stay fixed when the model is pushed by its context, and whether some values tend to shift together. The authors build on the LITMUSVALUES setup. Models face two-action dilemmas, each action tied to one of 16 value classes drawn from developer principles such as Claude's Constitution and OpenAI's ModelSpec. Elo ratings computed from these pairwise "value battles" produce a ranking. They then apply six prompting strategies meant to shift values (Direct instruction, Rubric, In-Context Learning, Scenario, Persuasion, Persona) to eight models from three families. Pearson correlations of the resulting rank changes reveal how values are linked. The main findings are that larger models are easier to steer than smaller ones, and that immersive Scenario prompts reorder rankings most strongly. Scenario prompts can also anchor a model's values against later manipulation. The work matters for alignment because more capable models may be more open to contextual value manipulation.

## Key points
- Benchmark: 6 value-transformation prompting methods tested on 8 LLMs from 3 families. The figures show GPT-4.1-mini/nano, Qwen3 and LLaMA variants. Value rankings come from Elo scores over dilemmas spanning 16 value classes.
- Some values rise or fall together under perturbation, for example Privacy and Respect. The authors call this structure a "value correlation topology."
- Value-correlation patterns are more alike between large models from different families than between small models from the same family. The authors link this to the Platonic Representation Hypothesis.
- Defense tests used Qwen2.5-7B/32B/72B. Scenario framing, such as movie settings like "Avengers" or "Inception," weakened later Persona attacks in the larger models, while the 7B model shifted more. The paper proposes three protocols: Contextual Red-teaming, Synergistic Alignment, and a Defensive Context Framework.
- Ablation: the authors built a debiased dataset with 25 values and balanced frequencies, using gpt-3.5-turbo-0125 and manual selection of 3,000 dilemmas. The plasticity effect held on this data, including for DeepSeek-V3.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Zhenheng Tang, Qihua Pan, Jingya Shen, Xiang Liu, Qian Wang, Bo Li and Xiaowen Chu. 2026. Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tang2026probing,
  title = {{Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable}},
  author = {Zhenheng Tang, Qihua Pan, Jingya Shen, Xiang Liu, Qian Wang, Bo Li and Xiaowen Chu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1715},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1715}
}
```

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## References

153 references, 43 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Gati V Aher, Rosa I Arriaga, and Adam Tauman Kalai. 2023. Using large language models to simulate multiple humans and replicate human subject studies. In International Conference on Machine Learning, pages 337–371. Proceedings of Machine Learning Research. [find](https://scholar.google.com/scholar?q=Gati+V+Aher%2C+Rosa+I+Arriaga%2C+and+Adam+Tauman+Kalai%2E+2023%2E+Using+large+language+models+to+simulate+multiple+humans+and+replicate+human+subject+studies%2E+In+International+Conference+on+Machine+Learning%2C+)
2. Anthropic. 2024. Claude’s Constitution. [https://www.anthropic.com/news/claudes-constitution](https://www.anthropic.com/news/claudes-constitution). Published: 2024-05-09; Accessed: 2024-05-19. [↗](https://www.anthropic.com/news/claudes-constitution)
3. Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate. 2023. [Out of one, many: Using language models to simulate human samples](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/). Political Analysis, 31(3):337–351.
4. Simran Arora, Avanika Narayan, Mayee F Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, and Christopher Re. 2022. Ask me anything: A simple strategy for prompting language models. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Simran+Arora%2C+Avanika+Narayan%2C+Mayee+F+Chen%2C+Laurel+Orr%2C+Neel+Guha%2C+Kush+Bhatia%2C+Ines+Chami%2C+and+Christopher+Re%2E+2022%2E+Ask+me+anything%3A+A+simple+strategy+for+prompting+language+models%2E+In+The+Eleventh)
5. Isaac Asimov. 1950. Three laws of robotics. [find](https://scholar.google.com/scholar?q=Isaac+Asimov%2E+1950%2E+Three+laws+of+robotics%2E)
6. Haonan Bian, Zhiyuan Yao, Sen Hu, Zishan Xu, Shaolei Zhang, Yifu Guo, Ziliang Yang, Xueran Han, Huacan Wang, and Ronghao Chen. 2026. Realmem: Benchmarking llms in real-world memory-driven interaction. Preprint, arXiv:2601.06966. [find](https://scholar.google.com/scholar?q=Haonan+Bian%2C+Zhiyuan+Yao%2C+Sen+Hu%2C+Zishan+Xu%2C+Shaolei+Zhang%2C+Yifu+Guo%2C+Ziliang+Yang%2C+Xueran+Han%2C+Huacan+Wang%2C+and+Ronghao+Chen%2E+2026%2E+Realmem%3A+Benchmarking+llms+in+real%2Dworld+memory%2Ddriven+interaction%2E)
7. Marcel Binz and Eric Schulz. 2023. Using cognitive psychology to understand gpt-3. Proceedings of the National Academy of Sciences, 120(6). [find](https://scholar.google.com/scholar?q=Marcel+Binz+and+Eric+Schulz%2E+2023%2E+Using+cognitive+psychology+to+understand+gpt%2D3%2E+Proceedings+of+the+National+Academy+of+Sciences%2C+120%286%29%2E)
8. Alexander Bondarenko, Denis Volk, Dmitrii Volkov, and Jeffrey Ladish. 2025. Demonstrating specification gaming in reasoning models. arXiv preprint arXiv:2502.13295. — [arXiv:2502.13295](https://arxiv.org/abs/2502.13295) [find](https://scholar.google.com/scholar?q=Alexander+Bondarenko%2C+Denis+Volk%2C+Dmitrii+Volkov%2C+and+Jeffrey+Ladish%2E+2025%2E+Demonstrating+specification+gaming+in+reasoning+models%2E+arXiv+preprint+arXiv%3A2502%2E13295%2E+%E2%80%94+%5BarXiv%3A2502%2E13295%5D%28https%3A%2F%2Farxiv%2E)
9. Joseph Carlsmith. 2022. Is power-seeking ai an existential risk? arXiv preprint arXiv:2206.13353. — [arXiv:2206.13353](https://arxiv.org/abs/2206.13353) [find](https://scholar.google.com/scholar?q=Joseph+Carlsmith%2E+2022%2E+Is+power%2Dseeking+ai+an+existential+risk%3F+arXiv+preprint+arXiv%3A2206%2E13353%2E+%E2%80%94+%5BarXiv%3A2206%2E13353%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2206%2E13353%29)
10. Kushal Chawla, Weiyan Shi, Jingwen Zhang, Gale Lucas, Zhou Yu, and Jonathan Gratch. 2023. Social influence dialogue systems: A survey of datasets and models for social influence tasks. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pag… [find](https://scholar.google.com/scholar?q=Kushal+Chawla%2C+Weiyan+Shi%2C+Jingwen+Zhang%2C+Gale+Lucas%2C+Zhou+Yu%2C+and+Jonathan+Gratch%2E+2023%2E+Social+influence+dialogue+systems%3A+A+survey+of+datasets+and+models+for+social+influence+tasks%2E+In+Proceedings+)
11. Canyu Chen and Kai Shu. 2023. Can llm-generated misinformation be detected? arXiv. [find](https://scholar.google.com/scholar?q=Canyu+Chen+and+Kai+Shu%2E+2023%2E+Can+llm%2Dgenerated+misinformation+be+detected%3F+arXiv%2E)
12. Sijing Chen, Lu Xiao, and Jin Mao. 2021. Persuasion strategies of misinformation-containing posts in the social media. Information Processing & Management, 58(5):102665. [find](https://scholar.google.com/scholar?q=Sijing+Chen%2C+Lu+Xiao%2C+and+Jin+Mao%2E+2021%2E+Persuasion+strategies+of+misinformation%2Dcontaining+posts+in+the+social+media%2E+Information+Processing+%26+Management%2C+58%285%29%3A102665%2E)
13. Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. 2023. [Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/). arXiv preprint.
14. Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, and Shujian Huang. 2023. A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily. arxiv. [find](https://scholar.google.com/scholar?q=Peng+Ding%2C+Jun+Kuang%2C+Dan+Ma%2C+Xuezhi+Cao%2C+Yunsen+Xian%2C+Jiajun+Chen%2C+and+Shujian+Huang%2E+2023%2E+A+Wolf+in+Sheep%E2%80%99s+Clothing%3A+Generalized+Nested+Jailbreak+Prompts+can+Fool+Large+Language+Models+Easily%2E+arx)
15. Peijie Dong, Zhenheng Tang, Xiang Liu, Lujun Li, Xiaowen Chu, and Bo Li. 2025. Can compressed llms truly act? an empirical evaluation of agentic capabilities in llm compression. In Proceedings of the 42th International Conference on Machine Learning, Proceedings of Machine Learning Research. PMLR. [find](https://scholar.google.com/scholar?q=Peijie+Dong%2C+Zhenheng+Tang%2C+Xiang+Liu%2C+Lujun+Li%2C+Xiaowen+Chu%2C+and+Bo+Li%2E+2025%2E+Can+compressed+llms+truly+act%3F+an+empirical+evaluation+of+agentic+capabilities+in+llm+compression%2E+In+Proceedings+of+the+)
16. Yixiang Chen, Penglei Sun, Xiang Li, and Xiaowen Chu. 2025a. Mrd-rag: enhancing medical diagnosis with multi-round retrieval-augmented generation. arXiv e-prints, pages arXiv–2504. [find](https://scholar.google.com/scholar?q=Yixiang+Chen%2C+Penglei+Sun%2C+Xiang+Li%2C+and+Xiaowen+Chu%2E+2025a%2E+Mrd%2Drag%3A+enhancing+medical+diagnosis+with+multi%2Dround+retrieval%2Daugmented+generation%2E+arXiv+e%2Dprints%2C+pages+arXiv%E2%80%932504%2E)
17. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, and 1 others. 2022. [A survey on in-context learning](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/). arXiv preprint arXiv:2301.00234.
18. Zhijun Chen, Jingzheng Li, Pengpeng Chen, Zhuoran Li, Kai Sun, Yuankai Luo, Qianren Mao, Dingqi Yang, Hailong Sun, and Philip S Yu. 2025b. Harnessing multiple large language models: A survey on llm ensemble. arXiv preprint arXiv:2502.18036. — [arXiv:2502.18036](https://arxiv.org/abs/2502.18036) [find](https://scholar.google.com/scholar?q=Zhijun+Chen%2C+Jingzheng+Li%2C+Pengpeng+Chen%2C+Zhuoran+Li%2C+Kai+Sun%2C+Yuankai+Luo%2C+Qianren+Mao%2C+Dingqi+Yang%2C+Hailong+Sun%2C+and+Philip+S+Yu%2E+2025b%2E+Harnessing+multiple+large+language+models%3A+A+survey+on+llm+en)
19. Esin Durmus, Karina Nguyen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli. 2024. [Towards measuring t…](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
20. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios N Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I Jordan, Joseph E Gonzalez, and 1 others. 2024. [Chatbot arena: an open platform for evaluating llms by human preference](https://md.research.elamur.ai/free/en/papers/chiang-2024-chatbot-arena-open-platform-evaluating/). In Proceedings of the 41st International Conference o…
21. Yu Ying Chiu, Liwei Jiang, and Yejin Choi. 2025a. Dailydilemmas: Revealing value preferences of llms with quandaries of daily life. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yu+Ying+Chiu%2C+Liwei+Jiang%2C+and+Yejin+Choi%2E+2025a%2E+Dailydilemmas%3A+Revealing+value+preferences+of+llms+with+quandaries+of+daily+life%2E+In+The+Thirteenth+International+Conference+on+Learning+Representatio)
22. Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, and Yejin Choi. 2024. Culturalbench: a robust, diverse and challenging benchmark on measuring the (lack of) cultural knowledge of llms. Preprint,… — [arXiv:2410.02677](https://arxiv.org/abs/2410.02677) [find](https://scholar.google.com/scholar?q=Yu+Ying+Chiu%2C+Liwei+Jiang%2C+Bill+Yuchen+Lin%2C+Chan+Young+Park%2C+Shuyue+Stella+Li%2C+Sahithya+Ravi%2C+Mehar+Bhatia%2C+Maria+Antoniak%2C+Yulia+Tsvetkov%2C+Vered+Shwartz%2C+and+Yejin+Choi%2E+2024%2E+Culturalbench%3A+a+robust)
23. Yu Ying Chiu, Zhilin Wang, Sharan Maiya, Yejin Choi, Kyle Fish, Sydney Levine, and Evan Hubinger. 2025b. Will ai tell lies to save sick children? litmus-testing ai values prioritization with airiskdilemmas. arXiv preprint arXiv:2505.14633. — [arXiv:2505.14633](https://arxiv.org/abs/2505.14633) [find](https://scholar.google.com/scholar?q=Yu+Ying+Chiu%2C+Zhilin+Wang%2C+Sharan+Maiya%2C+Yejin+Choi%2C+Kyle+Fish%2C+Sydney+Levine%2C+and+Evan+Hubinger%2E+2025b%2E+Will+ai+tell+lies+to+save+sick+children%3F+litmus%2Dtesting+ai+values+prioritization+with+airiskdil)
24. Kaat De Corte, John Cairns, and Richard Grieve. 2021. Stated versus revealed preferences: An approach to reduce bias. Health economics, 30(5):1095–1123. [find](https://scholar.google.com/scholar?q=Kaat+De+Corte%2C+John+Cairns%2C+and+Richard+Grieve%2E+2021%2E+Stated+versus+revealed+preferences%3A+An+approach+to+reduce+bias%2E+Health+economics%2C+30%285%29%3A1095%E2%80%931123%2E)
25. Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, and Yang Liu. 2024. Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning. arxiv. [find](https://scholar.google.com/scholar?q=Gelei+Deng%2C+Yi+Liu%2C+Kailong+Wang%2C+Yuekang+Li%2C+Tianwei+Zhang%2C+and+Yang+Liu%2E+2024%2E+Pandora%3A+Jailbreak+GPTs+by+Retrieval+Augmented+Generation+Poisoning%2E+arxiv%2E)
26. Paul Eastwick, Jehan Sparks, Eli Finkel, Eva Meza, Matúš Adamkovič, Ting Ai, Aderonke Akintola, Laith Al-Shawaf, Denisa Apriliawati, Patricia Arriaga, Benjamin Aubert-Teillaud, Gabriel Baník, Krystian Barzykowski, Jan Röer, Ivan Ropovik, Robert Ross, Ezgi Sakman, Cristina Salvador, and Dmitry Grigo… [find](https://scholar.google.com/scholar?q=Paul+Eastwick%2C+Jehan+Sparks%2C+Eli+Finkel%2C+Eva+Meza%2C+Mat%C3%BA%C5%A1+Adamkovic%CC%8C%2C+Ting+Ai%2C+Aderonke+Akintola%2C+Laith+Al%2DShawaf%2C+Denisa+Apriliawati%2C+Patricia+Arriaga%2C+Benjamin+Aubert%2DTeillaud%2C+Gabriel+Ban%C3%ADk%2C+Krystia)
27. Ullrich KH Ecker, Stephan Lewandowsky, John Cook, Philipp Schmid, Lisa K Fazio, Nadia Brashier, Panayiota Kendeou, Emily K Vraga, and Michelle A Amazeen. 2022. The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1):13–29. [find](https://scholar.google.com/scholar?q=Ullrich+KH+Ecker%2C+Stephan+Lewandowsky%2C+John+Cook%2C+Philipp+Schmid%2C+Lisa+K+Fazio%2C+Nadia+Brashier%2C+Panayiota+Kendeou%2C+Emily+K+Vraga%2C+and+Michelle+A+Amazeen%2E+2022%2E+The+psychological+drivers+of+misinformat)
28. Ronald Fischer, Markus Luczak-Roesch, and Johannes A Karl. 2023. What does chatgpt return about human values? exploring value bias in chatgpt using a descriptive value theory. arXiv preprint. [find](https://scholar.google.com/scholar?q=Ronald+Fischer%2C+Markus+Luczak%2DRoesch%2C+and+Johannes+A+Karl%2E+2023%2E+What+does+chatgpt+return+about+human+values%3F+exploring+value+bias+in+chatgpt+using+a+descriptive+value+theory%2E+arXiv+preprint%2E)
29. Robert H Gass and John S Seiter. 2015. Persuasion: Social inflence and compliance gaining. Routledge. [find](https://scholar.google.com/scholar?q=Robert+H+Gass+and+John+S+Seiter%2E+2015%2E+Persuasion%3A+Social+inflence+and+compliance+gaining%2E+Routledge%2E)
30. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
31. Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, and 1 others. 2024. Alignment faking in large language models. arXiv preprint arXiv:2412.14093. [find](https://scholar.google.com/scholar?q=Ryan+Greenblatt%2C+Carson+Denison%2C+Benjamin+Wright%2C+Fabien+Roger%2C+Monte+MacDiarmid%2C+Sam+Marks%2C+Johannes+Treutlein%2C+Tim+Belonax%2C+Jack+Chen%2C+David+Duvenaud%2C+and+1+others%2E+2024%2E+Alignment+faking+in+large+l)
32. Maanak Gupta, Charankumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj. 2023. From Chat-GPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy. arxiv. [find](https://scholar.google.com/scholar?q=Maanak+Gupta%2C+Charankumar+Akiri%2C+Kshitiz+Aryal%2C+Eli+Parker%2C+and+Lopamudra+Praharaj%2E+2023%2E+From+Chat%2DGPT+to+ThreatGPT%3A+Impact+of+Generative+AI+in+Cybersecurity+and+Privacy%2E+arxiv%2E)
33. Dorit Hadar-Shoval, Kfir Asraf, Yonathan Mizrachi, Yuval Haber, and Zohar Elyoseph. 2024. Assessing the alignment of large language models with human values for mental health integration: Cross-sectional study using schwartz’s theory of basic values. JMIR Mental Health, 11. [find](https://scholar.google.com/scholar?q=Dorit+Hadar%2DShoval%2C+Kfir+Asraf%2C+Yonathan+Mizrachi%2C+Yuval+Haber%2C+and+Zohar+Elyoseph%2E+2024%2E+Assessing+the+alignment+of+large+language+models+with+human+values+for+mental+health+integration%3A+Cross%2Dsectio)
34. Dorit Hadar-Shoval, Zohar Elyoseph, and Maya Lvovsky. 2023. The plasticity of chatgpt’s mentalizing abilities: Personalization for personality structures. Frontiers in Psychiatry, 14:1234397. [find](https://scholar.google.com/scholar?q=Dorit+Hadar%2DShoval%2C+Zohar+Elyoseph%2C+and+Maya+Lvovsky%2E+2023%2E+The+plasticity+of+chatgpt%E2%80%99s+mentalizing+abilities%3A+Personalization+for+personality+structures%2E+Frontiers+in+Psychiatry%2C+14%3A1234397%2E)
35. Jonathan Haidt. 2012. The righteous mind. Random House, New York, NY. [find](https://scholar.google.com/scholar?q=Jonathan+Haidt%2E+2012%2E+The+righteous+mind%2E+Random+House%2C+New+York%2C+NY%2E)
36. Helia Hashemi, Jason Eisner, Corby Rosset, Benjamin Van Durme, and Chris Kedzie. 2024. [LLM-rubric: A multidimensional, calibrated approach to automated evaluation of natural language texts](https://md.research.elamur.ai/free/en/papers/hashemi-2024-llm-rubric-multidimensional-calibrated-approach/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pap…
37. Dan Hendrycks, Mantas Mazeika, and Thomas Woodside. 2023. An overview of catastrophic ai risks. arXiv preprint arXiv:2306.12001. — [arXiv:2306.12001](https://arxiv.org/abs/2306.12001) [find](https://scholar.google.com/scholar?q=Dan+Hendrycks%2C+Mantas+Mazeika%2C+and+Thomas+Woodside%2E+2023%2E+An+overview+of+catastrophic+ai+risks%2E+arXiv+preprint+arXiv%3A2306%2E12001%2E+%E2%80%94+%5BarXiv%3A2306%2E12001%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2306%2E12001%29)
38. Sen Hu, Yuxiang Wei, Jiaxin Ran, Zhiyuan Yao, Xueran Han, Huacan Wang, Ronghao Chen, and Lei Zou. 2026a. Does memory need graphs? a unified framework and empirical analysis for long-term dialog memory. Preprint, arXiv:2601.01280. [find](https://scholar.google.com/scholar?q=Sen+Hu%2C+Yuxiang+Wei%2C+Jiaxin+Ran%2C+Zhiyuan+Yao%2C+Xueran+Han%2C+Huacan+Wang%2C+Ronghao+Chen%2C+and+Lei+Zou%2E+2026a%2E+Does+memory+need+graphs%3F+a+unified+framework+and+empirical+analysis+for+long%2Dterm+dialog+memory)
39. Sen Hu, Zhiyu Zhang, Yuxiang Wei, Xueran Han, Zhenheng Tang, Huacan Wang, and Ronghao Chen. 2026b. Clonemem: Benchmarking long-term memory for ai clones. Preprint, arXiv:2601.07023. [find](https://scholar.google.com/scholar?q=Sen+Hu%2C+Zhiyu+Zhang%2C+Yuxiang+Wei%2C+Xueran+Han%2C+Zhenheng+Tang%2C+Huacan+Wang%2C+and+Ronghao+Chen%2E+2026b%2E+Clonemem%3A+Benchmarking+long%2Dterm+memory+for+ai+clones%2E+Preprint%2C+arXiv%3A2601%2E07023%2E)
40. Yuncheng Hua, Lizhen Qu, Zhuang Li, Hao Xue, Flora D Salim, and Gholamreza Haffari. 2025. Ride: Enhancing large language model alignment through restyled in-context learning demonstration exemplars. arXiv preprint arXiv:2502.11681. — [arXiv:2502.11681](https://arxiv.org/abs/2502.11681) [find](https://scholar.google.com/scholar?q=Yuncheng+Hua%2C+Lizhen+Qu%2C+Zhuang+Li%2C+Hao+Xue%2C+Flora+D+Salim%2C+and+Gholamreza+Haffari%2E+2025%2E+Ride%3A+Enhancing+large+language+model+alignment+through+restyled+in%2Dcontext+learning+demonstration+exemplars%2E+a)
41. Saffron Huang, Esin Durmus, Miles McCain, Kunal Handa, Alex Tamkin, Jerry Hong, Michael Stern, Arushi Somani, Xiuruo Zhang, and Deep Ganguli. 2025a. Values in the wild: Discovering and analyzing values in real-world language model interactions. arXiv preprint arXiv:2504.15236. — [arXiv:2504.15236](https://arxiv.org/abs/2504.15236) [find](https://scholar.google.com/scholar?q=Saffron+Huang%2C+Esin+Durmus%2C+Miles+McCain%2C+Kunal+Handa%2C+Alex+Tamkin%2C+Jerry+Hong%2C+Michael+Stern%2C+Arushi+Somani%2C+Xiuruo+Zhang%2C+and+Deep+Ganguli%2E+2025a%2E+Values+in+the+wild%3A+Discovering+and+analyzing+value)
42. Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen. 2024. [Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/). In International Conference on Learning Representations (ICLR).
43. Zenan Huang, Yihong Zhuang, Guoshan Lu, Zeyu Qin, Haokai Xu, Tianyu Zhao, Ru Peng, Jiaqi Hu, Zhanming Shen, Xiaomeng Hu, and 1 others. 2025b. Reinforcement learning with rubric anchors. arXiv preprint arXiv:2508.12790. — [arXiv:2508.12790](https://arxiv.org/abs/2508.12790) [find](https://scholar.google.com/scholar?q=Zenan+Huang%2C+Yihong+Zhuang%2C+Guoshan+Lu%2C+Zeyu+Qin%2C+Haokai+Xu%2C+Tianyu+Zhao%2C+Ru+Peng%2C+Jiaqi+Hu%2C+Zhanming+Shen%2C+Xiaomeng+Hu%2C+and+1+others%2E+2025b%2E+Reinforcement+learning+with+rubric+anchors%2E+arXiv+preprint)
44. Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M Ziegler, Tim Maxwell, Newton Cheng, and 1 others. 2024. Sleeper agents: Training deceptive llms that persist through safety training. arXiv preprint arXiv:2401.05566. [find](https://scholar.google.com/scholar?q=Evan+Hubinger%2C+Carson+Denison%2C+Jesse+Mu%2C+Mike+Lambert%2C+Meg+Tong%2C+Monte+MacDiarmid%2C+Tamera+Lanham%2C+Daniel+M+Ziegler%2C+Tim+Maxwell%2C+Newton+Cheng%2C+and+1+others%2E+2024%2E+Sleeper+agents%3A+Training+deceptive+ll)
45. Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola. 2024. Position: The platonic representation hypothesis. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, pages 20617–20642. PMLR. [find](https://scholar.google.com/scholar?q=Minyoung+Huh%2C+Brian+Cheung%2C+Tongzhou+Wang%2C+and+Phillip+Isola%2E+2024%2E+Position%3A+The+platonic+representation+hypothesis%2E+In+Proceedings+of+the+41st+International+Conference+on+Machine+Learning%2C+volume+23)
46. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Computing Surveys, 55(12):1–38.
47. Bohan Jiang, Zhen Tan, Ayushi Nirmal, and Huan Liu. 2023a. Disinformation detection: An evolving challenge in the age of llms. arXiv. [find](https://scholar.google.com/scholar?q=Bohan+Jiang%2C+Zhen+Tan%2C+Ayushi+Nirmal%2C+and+Huan+Liu%2E+2023a%2E+Disinformation+detection%3A+An+evolving+challenge+in+the+age+of+llms%2E+arXiv%2E)
48. Hang Jiang, Xiajie Zhang, Xubo Cao, Jad Kabbara, and Deb Roy. 2023b. Personallm: Investigating the ability of gpt-3.5 to express personality traits and gender differences. arXiv. [find](https://scholar.google.com/scholar?q=Hang+Jiang%2C+Xiajie+Zhang%2C+Xubo+Cao%2C+Jad+Kabbara%2C+and+Deb+Roy%2E+2023b%2E+Personallm%3A+Investigating+the+ability+of+gpt%2D3%2E5+to+express+personality+traits+and+gender+differences%2E+arXiv%2E)
49. Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. [How can we know what language models know?](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/) Transactions of the Association for Computational Linguistics, 8:423–438.
50. Haoran Jin, Meng Li, Xiting Wang, Zhihao Xu, Minlie Huang, Yantao Jia, and Defu Lian. 2025. Internal value alignment in large language models through controlled value vector activation. arXiv preprint arXiv:2507.11316. [find](https://scholar.google.com/scholar?q=Haoran+Jin%2C+Meng+Li%2C+Xiting+Wang%2C+Zhihao+Xu%2C+Minlie+Huang%2C+Yantao+Jia%2C+and+Defu+Lian%2E+2025%2E+Internal+value+alignment+in+large+language+models+through+controlled+value+vector+activation%2E+arXiv+preprint)
51. Erik Jones, Anca D. Dragan, Aditi Raghunathan, and Jacob Steinhardt. 2023. Automatically Auditing Large Language Models via Discrete Optimization. In International Conference on Machine Learning (ICML), pages 15307–15329. PMLR. [find](https://scholar.google.com/scholar?q=Erik+Jones%2C+Anca+D%2E+Dragan%2C+Aditi+Raghunathan%2C+and+Jacob+Steinhardt%2E+2023%2E+Automatically+Auditing+Large+Language+Models+via+Discrete+Optimization%2E+In+International+Conference+on+Machine+Learning+%28ICML)
52. Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto. 2023. Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. arxiv. [find](https://scholar.google.com/scholar?q=Daniel+Kang%2C+Xuechen+Li%2C+Ion+Stoica%2C+Carlos+Guestrin%2C+Matei+Zaharia%2C+and+Tatsunori+Hashimoto%2E+2023%2E+Exploiting+Programmatic+Behavior+of+LLMs%3A+Dual%2DUse+Through+Standard+Security+Attacks%2E+arxiv%2E)
53. Yipeng Kang, Junqi Wang, Yexin Li, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Tingjun Wu, Xue Feng, Fangwei Zhong, and Zilong Zheng. 2025. [find](https://scholar.google.com/scholar?q=Yipeng+Kang%2C+Junqi+Wang%2C+Yexin+Li%2C+Mengmeng+Wang%2C+Wenming+Tu%2C+Quansen+Wang%2C+Hengli+Li%2C+Tingjun+Wu%2C+Xue+Feng%2C+Fangwei+Zhong%2C+and+Zilong+Zheng%2E+2025%2E)
54. Are the values of LLMs structurally aligned with humans? a causal perspective. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23147–23161, Vienna, Austria. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Are+the+values+of+LLMs+structurally+aligned+with+humans%3F+a+causal+perspective%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2025%2C+pages+23147%E2%80%9323161%2C+Vienna%2C+Austria%2E+Association+f)
55. Nora Kassner and Hinrich Schütze. 2020. [Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7811–7818, Online. Association for Computational Linguistics.
56. Celeste Kidd and Abeba Birhane. 2023. How ai can distort human beliefs. Science, 380(6651):1222–1223. [find](https://scholar.google.com/scholar?q=Celeste+Kidd+and+Abeba+Birhane%2E+2023%2E+How+ai+can+distort+human+beliefs%2E+Science%2C+380%286651%29%3A1222%E2%80%931223%2E)
57. Xiang Li, Penglei Sun, Wanyun Zhou, Zikai Wei, Yongqi Zhang, and Xiaowen Chu. 2025. Finkario: Event-enhanced automated construction of financial knowledge graph. arXiv preprint arXiv:2508.00961. [find](https://scholar.google.com/scholar?q=Xiang+Li%2C+Penglei+Sun%2C+Wanyun+Zhou%2C+Zikai+Wei%2C+Yongqi+Zhang%2C+and+Xiaowen+Chu%2E+2025%2E+Finkario%3A+Event%2Denhanced+automated+construction+of+financial+knowledge+graph%2E+arXiv+preprint+arXiv%3A2508%2E00961%2E)
58. Xiang Li, Zikai Wei, Yiyan Qi, Wanyun Zhou, Xiang Liu, Penglei Sun, Jian Guo, Yongqi Zhang, and Xiaowen Chu. 2026. Janus-q: End-to-end event-driven trading via hierarchical-gated reward modeling. arXiv preprint arXiv:2602.19919. — [arXiv:2602.19919](https://arxiv.org/abs/2602.19919) [find](https://scholar.google.com/scholar?q=Xiang+Li%2C+Zikai+Wei%2C+Yiyan+Qi%2C+Wanyun+Zhou%2C+Xiang+Liu%2C+Penglei+Sun%2C+Jian+Guo%2C+Yongqi+Zhang%2C+and+Xiaowen+Chu%2E+2026%2E+Janus%2Dq%3A+End%2Dto%2Dend+event%2Ddriven+trading+via+hierarchical%2Dgated+reward+modeling%2E+arXi)
59. Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, and Bo Han. 2023c. [DeepInception: Hypnotize Large Language Model to Be Jailbreaker](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/). arXiv preprint arXiv:2311.03191.
60. Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Bean, Katerina Margatina, Juan Ciro, Rafael Mosquera, Max Bartolo, Adina Williams, He He, and 1 others. 2024a. The prism alignment project: What participatory, representative and individualised human feedback reveals about the subjective a… [find](https://scholar.google.com/scholar?q=Hannah+Rose+Kirk%2C+Alexander+Whitefield%2C+Paul+R%C3%B6ttger%2C+Andrew+Bean%2C+Katerina+Margatina%2C+Juan+Ciro%2C+Rafael+Mosquera%2C+Max+Bartolo%2C+Adina+Williams%2C+He+He%2C+and+1+others%2E+2024a%2E+The+prism+alignment+project%3A)
61. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. [TruthfulQA: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics…
62. Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Michael Bean, Katerina Margatina, Rafael Mosquera, Juan Manuel Ciro, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott A. Hale. 2024b. The PRISM alignment dataset: What participatory, representative and individualised human feed… [find](https://scholar.google.com/scholar?q=Hannah+Rose+Kirk%2C+Alexander+Whitefield%2C+Paul+R%C3%B6ttger%2C+Andrew+Michael+Bean%2C+Katerina+Margatina%2C+Rafael+Mosquera%2C+Juan+Manuel+Ciro%2C+Max+Bartolo%2C+Adina+Williams%2C+He+He%2C+Bertie+Vidgen%2C+and+Scott+A%2E+Hale%2E+)
63. Xiang Liu, Zhenheng Tang, Peijie Dong, Zeyu Li, Bo Li, Xuming Hu, and Xiaowen Chu. 2025. Chunkkv: Semantic-preserving kv cache compression for efficient long-context llm inference. arxiv preprint arXiv:2502.00299. [find](https://scholar.google.com/scholar?q=Xiang+Liu%2C+Zhenheng+Tang%2C+Peijie+Dong%2C+Zeyu+Li%2C+Bo+Li%2C+Xuming+Hu%2C+and+Xiaowen+Chu%2E+2025%2E+Chunkkv%3A+Semantic%2Dpreserving+kv+cache+compression+for+efficient+long%2Dcontext+llm+inference%2E+arxiv+preprint+arXi)
64. Grgur Kovač, Masataka Sawayama, Rémy Portelas, Cédric Colas, Peter Ford Dominey, and Pierre-Yves Oudeyer. 2023. Large language models as superpositions of cultural perspectives. arXiv preprint arXiv:2307.07870. — [arXiv:2307.07870](https://arxiv.org/abs/2307.07870) [find](https://scholar.google.com/scholar?q=Grgur+Kovac%CC%8C%2C+Masataka+Sawayama%2C+R%C3%A9my+Portelas%2C+C%C3%A9dric+Colas%2C+Peter+Ford+Dominey%2C+and+Pierre%2DYves+Oudeyer%2E+2023%2E+Large+language+models+as+superpositions+of+cultural+perspectives%2E+arXiv+preprint+arXiv%3A)
65. Grgur Kovač, Rémy Portelas, Masataka Sawayama, Peter Ford Dominey, and Pierre-Yves Oudeyer. 2024. Stick to your role! stability of personal values expressed in large language models. PLOS ONE, 19(8). [find](https://scholar.google.com/scholar?q=Grgur+Kovac%CC%8C%2C+R%C3%A9my+Portelas%2C+Masataka+Sawayama%2C+Peter+Ford+Dominey%2C+and+Pierre%2DYves+Oudeyer%2E+2024%2E+Stick+to+your+role%21+stability+of+personal+values+expressed+in+large+language+models%2E+PLOS+ONE%2C+19%288%29%2E)
66. Bruce W. Lee, Yeongheon Lee, and Hyunsoo Cho. 2025. When prompting fails to sway: Inertia in moral and value judgments of large language models. Preprint, arXiv:2408.09049. [find](https://scholar.google.com/scholar?q=Bruce+W%2E+Lee%2C+Yeongheon+Lee%2C+and+Hyunsoo+Cho%2E+2025%2E+When+prompting+fails+to+sway%3A+Inertia+in+moral+and+value+judgments+of+large+language+models%2E+Preprint%2C+arXiv%3A2408%2E09049%2E)
67. Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish. 2023. Lora fine-tuning efficiently undoes safety training in llama 2-chat 70b. arxiv. [find](https://scholar.google.com/scholar?q=Simon+Lermen%2C+Charlie+Rogers%2DSmith%2C+and+Jeffrey+Ladish%2E+2023%2E+Lora+fine%2Dtuning+efficiently+undoes+safety+training+in+llama+2%2Dchat+70b%2E+arxiv%2E)
68. Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, and Katia Sycara. 2023a. [Theory of mind for multi-agent collaboration via large language models](https://md.research.elamur.ai/free/en/papers/li-2023-theory-mind-multi-agent-collaboration/). arXiv preprint arXiv:2310.10701.
69. Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023b. [Halueval: A large-scale hallucination evaluation benchmark for large language models](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/). arXiv.
70. Caroline Lindahl and Helin Saeid. 2023. Unveiling the values of ChatGPT: An explorative study on human values in AI systems. [find](https://scholar.google.com/scholar?q=Caroline+Lindahl+and+Helin+Saeid%2E+2023%2E+Unveiling+the+values+of+ChatGPT%3A+An+explorative+study+on+human+values+in+AI+systems%2E)
71. Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, Kailong Wang, and Yang Liu. 2024. [Jailbreaking chatgpt via prompt engineering: An empirical study](https://md.research.elamur.ai/free/en/papers/liu-2023-jailbreaking-chatgpt-prompt-engineering-empirical/). Preprint, arXiv:2305.13860.
72. Zhanzhi Lou, Hui Chen, Yibo Li, Qian Wang, and Bryan Hooi. 2026. Learning to learn-at-test-time: Language agents with learnable adaptation policies. arXiv preprint arXiv:2604.00830. — [arXiv:2604.00830](https://arxiv.org/abs/2604.00830) [find](https://scholar.google.com/scholar?q=Zhanzhi+Lou%2C+Hui+Chen%2C+Yibo+Li%2C+Qian+Wang%2C+and+Bryan+Hooi%2E+2026%2E+Learning+to+learn%2Dat%2Dtest%2Dtime%3A+Language+agents+with+learnable+adaptation+policies%2E+arXiv+preprint+arXiv%3A2604%2E00830%2E+%E2%80%94+%5BarXiv%3A2604%2E0083)
73. Huijie Lv, Xiao Wang, Yuansen Zhang, Caishuang Huang, Shihan Dou, Junjie Ye, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024. CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models. arxiv. [find](https://scholar.google.com/scholar?q=Huijie+Lv%2C+Xiao+Wang%2C+Yuansen+Zhang%2C+Caishuang+Huang%2C+Shihan+Dou%2C+Junjie+Ye%2C+Tao+Gui%2C+Qi+Zhang%2C+and+Xuanjing+Huang%2E+2024%2E+CodeChameleon%3A+Personalized+Encryption+Framework+for+Jailbreaking+Large+Langua)
74. Rokeach M. 1973. The nature of human values. Free press. [find](https://scholar.google.com/scholar?q=Rokeach+M%2E+1973%2E+The+nature+of+human+values%2E+Free+press%2E)
75. Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv. [find](https://scholar.google.com/scholar?q=Potsawee+Manakul%2C+Adian+Liusie%2C+and+Mark+JF+Gales%2E+2023%2E+Selfcheckgpt%3A+Zero%2Dresource+black%2Dbox+hallucination+detection+for+generative+large+language+models%2E+arXiv%2E)
76. Mantas Mazeika, Xuwang Yin, Rishub Tamirisa, Jaehyuk Lim, Bruce W Lee, Richard Ren, Long Phan, Norman Mu, Adam Khoja, Oliver Zhang, and 1 others. 2025. Utility engineering: Analyzing and controlling emergent value systems in ais. arXiv preprint arXiv:2502.08640. — [arXiv:2502.08640](https://arxiv.org/abs/2502.08640) [find](https://scholar.google.com/scholar?q=Mantas+Mazeika%2C+Xuwang+Yin%2C+Rishub+Tamirisa%2C+Jaehyuk+Lim%2C+Bruce+W+Lee%2C+Richard+Ren%2C+Long+Phan%2C+Norman+Mu%2C+Adam+Khoja%2C+Oliver+Zhang%2C+and+1+others%2E+2025%2E+Utility+engineering%3A+Analyzing+and+controlling+e)
77. Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022. [Rethinking the role of demonstrations: What makes in-context learning work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11048–11064, Ab…
78. Marilù Miotto, Nicola Rossberg, and Bennett Kleinberg. 2022. Who is GPT-3? an exploration of personality, values and demographics. arXiv. [find](https://scholar.google.com/scholar?q=Maril%C3%B9+Miotto%2C+Nicola+Rossberg%2C+and+Bennett+Kleinberg%2E+2022%2E+Who+is+GPT%2D3%3F+an+exploration+of+personality%2C+values+and+demographics%2E+arXiv%2E)
79. Jared Moore, Tanvi Deshpande, and Diyi Yang. 2024. Are large language models consistent over value-laden questions? arXiv preprint arXiv:2407.02996. [find](https://scholar.google.com/scholar?q=Jared+Moore%2C+Tanvi+Deshpande%2C+and+Diyi+Yang%2E+2024%2E+Are+large+language+models+consistent+over+value%2Dladen+questions%3F+arXiv+preprint+arXiv%3A2407%2E02996%2E)
80. OpenAI. 2025a. Model Spec. [https://model-spec.openai.com/2025-02-12.html](https://model-spec.openai.com/2025-02-12.html). Published: 2025-02-12; Accessed: 2025-02-12. [↗](https://model-spec.openai.com/2025-02-12.html)
81. R OpenAI. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv.
82. R OpenAI. 2025b. Introducing gpt-4.1 in the api. [https://openai.com/index/gpt-4-1/](https://openai.com/index/gpt-4-1/). [↗](https://openai.com/index/gpt-4-1/)
83. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). Advances in neural information processing systems, 35:27730–27744.
84. Ali Pakizeh, Jochen E Gebauer, and Gregory R Maio. 2007. Basic human values: Inter-value structure in memory. Journal of Experimental Social Psychology, 43(3):458–465. [find](https://scholar.google.com/scholar?q=Ali+Pakizeh%2C+Jochen+E+Gebauer%2C+and+Gregory+R+Maio%2E+2007%2E+Basic+human+values%3A+Inter%2Dvalue+structure+in+memory%2E+Journal+of+Experimental+Social+Psychology%2C+43%283%29%3A458%E2%80%93465%2E)
85. Max Pellert, Clemens M Lechner, Claudia Wagner, Beatrice Rammstedt, and Markus Strohmaier. 2024. Ai psychometrics: Assessing the psychological profiles of large language models through psychometric inventories. Perspectives on Psychological Science, 19(5):808–826. [find](https://scholar.google.com/scholar?q=Max+Pellert%2C+Clemens+M+Lechner%2C+Claudia+Wagner%2C+Beatrice+Rammstedt%2C+and+Markus+Strohmaier%2E+2024%2E+Ai+psychometrics%3A+Assessing+the+psychological+profiles+of+large+language+models+through+psychometric+in)
86. Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019. [Language models as knowledge bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural…
87. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023. [Fine-tuning aligned language models compromises safety, even when users do not intend to!](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/) arxiv.
88. Guanghui Qin and Jason Eisner. 2021. [Learning how to ask: Querying LMs with mixtures of soft prompts](https://md.research.elamur.ai/free/en/papers/qin-2021-learning-ask-querying-lms-mixtures/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5203–5212, Online. Association for Computatio…
89. Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2024. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Preprint, arXiv:2305.18290.
90. Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023. [Investigating the factual knowledge boundary of large language models with retrieval augmentation](https://md.research.elamur.ai/free/en/papers/ren-2025-investigating-factual-knowledge-boundary-large/). arXiv.
91. Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How much knowledge can you pack into the parameters of a language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5418–5426, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Adam+Roberts%2C+Colin+Raffel%2C+and+Noam+Shazeer%2E+2020%2E+How+much+knowledge+can+you+pack+into+the+parameters+of+a+language+model%3F+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+Langu)
92. Brent W Roberts and Hee J Yoon. 2022. Personality psychology. Annual Review of Psychology, 73(1):489– 516. [find](https://scholar.google.com/scholar?q=Brent+W+Roberts+and+Hee+J+Yoon%2E+2022%2E+Personality+psychology%2E+Annual+Review+of+Psychology%2C+73%281%29%3A489%E2%80%93+516%2E)
93. Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, and Ella Daniel. 2024. Do llms have consistent values? arXiv preprint arXiv:2407.12878. — [arXiv:2407.12878](https://arxiv.org/abs/2407.12878) [find](https://scholar.google.com/scholar?q=Naama+Rozen%2C+Liat+Bezalel%2C+Gal+Elidan%2C+Amir+Globerson%2C+and+Ella+Daniel%2E+2024%2E+Do+llms+have+consistent+values%3F+arXiv+preprint+arXiv%3A2407%2E12878%2E+%E2%80%94+%5BarXiv%3A2407%2E12878%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2407%2E12878%29)
94. Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, and Ella Daniel. 2025. Do LLMs have consistent values? In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Naama+Rozen%2C+Liat+Bezalel%2C+Gal+Elidan%2C+Amir+Globerson%2C+and+Ella+Daniel%2E+2025%2E+Do+LLMs+have+consistent+values%3F+In+The+Thirteenth+International+Conference+on+Learning+Representations%2E)
95. Mustafa Safdari, Greg Serapio-García, Clément Crepy, Stephen Fitz, Peter Romero, Luning Sun, Marwa Abdulhai, Aleksandra Faust, and Maja Matarić. 2023. [Personality traits in large language models](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/). arXiv preprint arXiv:2307.00184.
96. Lilach Sagiv and Shalom H Schwartz. 2022. Personal values across cultures. Annual review of psychology, 73(1):517–546. [find](https://scholar.google.com/scholar?q=Lilach+Sagiv+and+Shalom+H+Schwartz%2E+2022%2E+Personal+values+across+cultures%2E+Annual+review+of+psychology%2C+73%281%29%3A517%E2%80%93546%2E)
97. Pouya Pezeshkpour and Estevam Hruschka. 2023. [Large language models sensitivity to the order of options in multiple-choice questions](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/). arXiv.
98. Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya, João Sedoc, Lyle H. Ungar, and Johannes C. Eichstaedt. 2024. Large language models show human-like social desirability biases in survey responses. Preprint, arXiv:2405.06058. — [arXiv:2405.06058](https://arxiv.org/abs/2405.06058) [find](https://scholar.google.com/scholar?q=Aadesh+Salecha%2C+Molly+E%2E+Ireland%2C+Shashanka+Subrahmanya%2C+Jo%C3%A3o+Sedoc%2C+Lyle+H%2E+Ungar%2C+and+Johannes+C%2E+Eichstaedt%2E+2024%2E+Large+language+models+show+human%2Dlike+social+desirability+biases+in+survey+respons)
99. Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. 2024. Personalizing reinforcement learning from human feedback with variational preference learning. Preprint, arXiv:2408.10075. — [arXiv:2408.10075](https://arxiv.org/abs/2408.10075) [find](https://scholar.google.com/scholar?q=Sriyash+Poddar%2C+Yanming+Wan%2C+Hamish+Ivison%2C+Abhishek+Gupta%2C+and+Natasha+Jaques%2E+2024%2E+Personalizing+reinforcement+learning+from+human+feedback+with+variational+preference+learning%2E+Preprint%2C+arXiv%3A240)
100. Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. 2024. In-context impersonation reveals large language models’ strengths and biases. Advances in Neural Information Processing Systems, 36. [find](https://scholar.google.com/scholar?q=Leonard+Salewski%2C+Stephan+Alaniz%2C+Isabel+Rio%2DTorto%2C+Eric+Schulz%2C+and+Zeynep+Akata%2E+2024%2E+In%2Dcontext+impersonation+reveals+large+language+models%E2%80%99+strengths+and+biases%2E+Advances+in+Neural+Information+Pr)
101. Paul A Samuelson. 1973. A note on the pure theory of consumer’s behaviour: an addendum. Economica. [find](https://scholar.google.com/scholar?q=Paul+A+Samuelson%2E+1973%2E+A+note+on+the+pure+theory+of+consumer%E2%80%99s+behaviour%3A+an+addendum%2E+Economica%2E)
102. Nino Scherrer, Claudia Shi, Amir Feder, and David M. Blei. 2023. Evaluating the moral beliefs encoded in llms. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. [find](https://scholar.google.com/scholar?q=Nino+Scherrer%2C+Claudia+Shi%2C+Amir+Feder%2C+and+David+M%2E+Blei%2E+2023%2E+Evaluating+the+moral+beliefs+encoded+in+llms%2E+In+Advances+in+Neural+Information+Processing+Systems+36%3A+Annual+Conference+on+Neural+Info)
103. Shalom H Schwartz. 1992. Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. In Advances in experimental social psychology, volume 25, pages 1–65. Elsevier. [find](https://scholar.google.com/scholar?q=Shalom+H+Schwartz%2E+1992%2E+Universals+in+the+content+and+structure+of+values%3A+Theoretical+advances+and+empirical+tests+in+20+countries%2E+In+Advances+in+experimental+social+psychology%2C+volume+25%2C+pages+1%E2%80%93)
104. Shalom H Schwartz. 2012a. An overview of the Schwartz theory of basic values. Online readings in Psychology and Culture, 2(1):1–20. [find](https://scholar.google.com/scholar?q=Shalom+H+Schwartz%2E+2012a%2E+An+overview+of+the+Schwartz+theory+of+basic+values%2E+Online+readings+in+Psychology+and+Culture%2C+2%281%29%3A1%E2%80%9320%2E)
105. Shalom H. Schwartz. 2012b. An overview of the schwartz theory of basic values. Online Readings in Psychology and Culture, 2:11. [find](https://scholar.google.com/scholar?q=Shalom+H%2E+Schwartz%2E+2012b%2E+An+overview+of+the+schwartz+theory+of+basic+values%2E+Online+Readings+in+Psychology+and+Culture%2C+2%3A11%2E)
106. Gregory Serapio-García, Mustafa Safdari, Madhumitha Panwar, Li Suyu, David Rincon, Joshua Midgley, Vicky Wang, Marwa Abdulhai, Sourav Farhan, Sandra C. Matz, and 1 others. 2025. A psychometric framework for evaluating and shaping personality traits in large language models. Nature Machine Intelligen… [find](https://scholar.google.com/scholar?q=Gregory+Serapio%2DGarc%C3%ADa%2C+Mustafa+Safdari%2C+Madhumitha+Panwar%2C+Li+Suyu%2C+David+Rincon%2C+Joshua+Midgley%2C+Vicky+Wang%2C+Marwa+Abdulhai%2C+Sourav+Farhan%2C+Sandra+C%2E+Matz%2C+and+1+others%2E+2025%2E+A+psychometric+framewo)
107. Greg Serapio-García, Mustafa Safdari, Clément Crepy, Luning Sun, Stephen Fitz, Peter Romero, Marwa Abdulhai, Aleksandra Faust, and Maja Matarić. 2025. [Personality traits in large language models](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/). Preprint, arXiv:2307.00184.
108. Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Scott Wen tau Yih. 2023. [Trusting your evidence: Hallucinate less with context-aware decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/). arXiv.
109. Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://md.research.elamur.ai/free/en/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4222–4235, Onlin…
110. Sonali Singh, Faranak Abri, and Akbar Siami Namin. 2023. Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles. In IEEE International Conference on Big Data (ICBD), pages 2508–2517. IEEE. [find](https://scholar.google.com/scholar?q=Sonali+Singh%2C+Faranak+Abri%2C+and+Akbar+Siami+Namin%2E+2023%2E+Exploiting+Large+Language+Models+%28LLMs%29+through+Deception+Techniques+and+Persuasion+Principles%2E+In+IEEE+International+Conference+on+Big+Data+%28I)
111. Ewa Skimina, Jan Cieciuch, and Włodzimierz Strus. 2021. Traits and values as predictors of the frequency of everyday behavior: Comparison between models and levels. Current Psychology, 40(1):133–153. [find](https://scholar.google.com/scholar?q=Ewa+Skimina%2C+Jan+Cieciuch%2C+and+W%C5%82odzimierz+Strus%2E+2021%2E+Traits+and+values+as+predictors+of+the+frequency+of+everyday+behavior%3A+Comparison+between+models+and+levels%2E+Current+Psychology%2C+40%281%29%3A133%E2%80%93153%2E)
112. Taylor Sorensen, Liwei Jiang, Jena D Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, and 1 others. 2024a. [Value kaleidoscope: Engaging ai with pluralistic human values, rights, and duties](https://md.research.elamur.ai/free/en/papers/sorensen-2023-value-kaleidoscope-engaging-ai-pluralistic/). In Proceedings of the AAAI Conference on Artificia…
113. Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, and Yejin Choi. 2024b. [A roadmap to pluralistic alignment](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/). Preprint, arXiv:2402.05070.
114. Claire Stevenson, Iris Smal, Matthijs Baas, Raoul Grasman, and Han van der Maas. 2022. Putting gpt-3’s creativity to the (alternative uses) test. arXiv preprint arXiv:2206.08932. — [arXiv:2206.08932](https://arxiv.org/abs/2206.08932) [find](https://scholar.google.com/scholar?q=Claire+Stevenson%2C+Iris+Smal%2C+Matthijs+Baas%2C+Raoul+Grasman%2C+and+Han+van+der+Maas%2E+2022%2E+Putting+gpt%2D3%E2%80%99s+creativity+to+the+%28alternative+uses%29+test%2E+arXiv+preprint+arXiv%3A2206%2E08932%2E+%E2%80%94+%5BarXiv%3A2206%2E08932%5D%28)
115. Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2020. [oLMpics-on what language model pre-training captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/). Transactions of the Association for Computational Linguistics, 8:743–758.
116. Zhenheng Tang, Xin He, Tiancheng Zhao, Fanjun-duo Wei, Xiang Liu, Peijie Dong, Qian Wang, Qi Li, Huacan Wang, Ronghao Chen, and 1 others. 2026a. Llm agent memory: A survey from a unified representation–management perspective. [find](https://scholar.google.com/scholar?q=Zhenheng+Tang%2C+Xin+He%2C+Tiancheng+Zhao%2C+Fanjun%2Dduo+Wei%2C+Xiang+Liu%2C+Peijie+Dong%2C+Qian+Wang%2C+Qi+Li%2C+Huacan+Wang%2C+Ronghao+Chen%2C+and+1+others%2E+2026a%2E+Llm+agent+memory%3A+A+survey+from+a+unified+representatio)
117. Zhenheng Tang, Zichen Tang, Junlin Huang, Xinglin Pan, Rudan Yan, Yuxin Wang, Amelie Chi Zhou, Shaohuai Shi, Xiaowen Chu, and Bo Li. 2025. Dreamddp: Accelerating data parallel distributed llm training with layer-wise scheduled partial synchronization. arXiv preprint arXiv:2502.11058. [find](https://scholar.google.com/scholar?q=Zhenheng+Tang%2C+Zichen+Tang%2C+Junlin+Huang%2C+Xinglin+Pan%2C+Rudan+Yan%2C+Yuxin+Wang%2C+Amelie+Chi+Zhou%2C+Shaohuai+Shi%2C+Xiaowen+Chu%2C+and+Bo+Li%2E+2025%2E+Dreamddp%3A+Accelerating+data+parallel+distributed+llm+training)
118. Zichen TANG, Zhenheng Tang, Gaoning Pan, Buhua Liu, Kunfeng Lai, Xiaowen Chu, and Bo Li. 2025. Ghost in the cloud: Your geo-distributed large language models training is easily manipulated. In ICML 2025 Workshop on Data in Generative Models - The Bad, the Ugly, and the Greats. [find](https://scholar.google.com/scholar?q=Zichen+TANG%2C+Zhenheng+Tang%2C+Gaoning+Pan%2C+Buhua+Liu%2C+Kunfeng+Lai%2C+Xiaowen+Chu%2C+and+Bo+Li%2E+2025%2E+Ghost+in+the+cloud%3A+Your+geo%2Ddistributed+large+language+models+training+is+easily+manipulated%2E+In+ICML+20)
119. Zichen Tang, Zirui Zhang, Qian Wang, Zhenheng Tang, Bo Li, and Xiaowen Chu. 2026b. Is your llm-as-a-recommender agent trustable? llms’ recommendation is easily hacked by biases (preferences). arXiv preprint arXiv:2603.17417. — [arXiv:2603.17417](https://arxiv.org/abs/2603.17417) [find](https://scholar.google.com/scholar?q=Zichen+Tang%2C+Zirui+Zhang%2C+Qian+Wang%2C+Zhenheng+Tang%2C+Bo+Li%2C+and+Xiaowen+Chu%2E+2026b%2E+Is+your+llm%2Das%2Da%2Drecommender+agent+trustable%3F+llms%E2%80%99+recommendation+is+easily+hacked+by+biases+%28preferences%29%2E+arXiv+pr)
120. Wen Lin Teh, Edimansyah Abdin, Asharani P.V., Fiona Devi Siva Kumar, Kumarasan Roystonn, Peizhi Wang, Saleha Shafie, Sherilyn Chang, Anitha Jeyagu-runathan, Janhavi Ajit Vaingankar, Chee Fang Sum, Eng Sing Lee, Rob M. van Dam, and Mythily Subramaniam. 2023. Measuring social desirability bias in a mu… [find](https://scholar.google.com/scholar?q=Wen+Lin+Teh%2C+Edimansyah+Abdin%2C+Asharani+P%2EV%2E%2C+Fiona+Devi+Siva+Kumar%2C+Kumarasan+Roystonn%2C+Peizhi+Wang%2C+Saleha+Shafie%2C+Sherilyn+Chang%2C+Anitha+Jeyagu%2Drunathan%2C+Janhavi+Ajit+Vaingankar%2C+Chee+Fang+Sum%2C+Eng)
121. Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023. [A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/). arXiv.
122. Qian Wang, Zhanzhi Lou, Zhenheng Tang, Nuo Chen, Xuandong Zhao, Wenxuan Zhang, Dawn Song, and Bingsheng He. 2025a. Assessing judging bias in large reasoning models: An empirical study. arXiv preprint arXiv:2504.09946. — [arXiv:2504.09946](https://arxiv.org/abs/2504.09946) [find](https://scholar.google.com/scholar?q=Qian+Wang%2C+Zhanzhi+Lou%2C+Zhenheng+Tang%2C+Nuo+Chen%2C+Xuandong+Zhao%2C+Wenxuan+Zhang%2C+Dawn+Song%2C+and+Bingsheng+He%2E+2025a%2E+Assessing+judging+bias+in+large+reasoning+models%3A+An+empirical+study%2E+arXiv+preprint+)
123. Qian Wang, Tianyu Wang, Zhenheng Tang, Qinbin Li, Nuo Chen, Jingsheng Liang, and Bingsheng He. 2025b. Megaagent: A large-scale autonomous llm-based multi-agent system without predefined sops. In The 63rd Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Qian+Wang%2C+Tianyu+Wang%2C+Zhenheng+Tang%2C+Qinbin+Li%2C+Nuo+Chen%2C+Jingsheng+Liang%2C+and+Bingsheng+He%2E+2025b%2E+Megaagent%3A+A+large%2Dscale+autonomous+llm%2Dbased+multi%2Dagent+system+without+predefined+sops%2E+In+The+6)
124. Qian Wang, Jiaying Wu, Zhenheng Tang, Bingqiao Luo, Nuo Chen, Wei Chen, and Bingsheng He. 2025c. What limits llm-based human simulation: Llms or our design? arXiv preprint arXiv:2501.08579. — [arXiv:2501.08579](https://arxiv.org/abs/2501.08579) [find](https://scholar.google.com/scholar?q=Qian+Wang%2C+Jiaying+Wu%2C+Zhenheng+Tang%2C+Bingqiao+Luo%2C+Nuo+Chen%2C+Wei+Chen%2C+and+Bingsheng+He%2E+2025c%2E+What+limits+llm%2Dbased+human+simulation%3A+Llms+or+our+design%3F+arXiv+preprint+arXiv%3A2501%2E08579%2E+%E2%80%94+%5BarXiv%3A2)
125. Xintao Wang, Yunze Xiao, Jen tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, and Yanghua Xiao. 2024. [Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews](https://md.research.elamur.ai/free/en/papers/wang-2024-incharacter-evaluating-personality-fidelity-role/). arXiv preprint arXiv:2310.17976.
126. Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023a. [Jailbroken: How does llm safety training fail?](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/) Preprint, arXiv:2307.02483.
127. Fanjunduo Wei, Zhenheng Tang, Rongfei Zeng, Tongliang Liu, Chengqi Zhang, Xiaowen Chu, and Bo Han. 2025. JailbreakloRA: Your downloaded loRA from sharing platforms might be unsafe. In ICML 2025 Workshop on Data in Generative Models - The Bad, the Ugly, and the Greats. [find](https://scholar.google.com/scholar?q=Fanjunduo+Wei%2C+Zhenheng+Tang%2C+Rongfei+Zeng%2C+Tongliang+Liu%2C+Chengqi+Zhang%2C+Xiaowen+Chu%2C+and+Bo+Han%2E+2025%2E+JailbreakloRA%3A+Your+downloaded+loRA+from+sharing+platforms+might+be+unsafe%2E+In+ICML+2025+Worksh)
128. Zeming Wei, Yifei Wang, and Yisen Wang. 2023b. [Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbreak-guard-aligned-language-models/). arxiv.
129. Tianyi Wu, Zhiwei Xue, Yue Liu, Jiaheng Zhang, Bryan Hooi, and See-Kiong Ng. 2025a. Geneshift: Impact of different scenario shift on jailbreaking llm. arXiv preprint arXiv:2504.08104. — [arXiv:2504.08104](https://arxiv.org/abs/2504.08104) [find](https://scholar.google.com/scholar?q=Tianyi+Wu%2C+Zhiwei+Xue%2C+Yue+Liu%2C+Jiaheng+Zhang%2C+Bryan+Hooi%2C+and+See%2DKiong+Ng%2E+2025a%2E+Geneshift%3A+Impact+of+different+scenario+shift+on+jailbreaking+llm%2E+arXiv+preprint+arXiv%3A2504%2E08104%2E+%E2%80%94+%5BarXiv%3A2504%2E08)
130. Tianyu Wu, Lingrui Mei, Ruibin Yuan, Lujun Li, Wei Xue, and Yike Guo. 2024. You know what i’m saying: Jailbreak attack via implicit reference. arXiv preprint arXiv:2410.03857. — [arXiv:2410.03857](https://arxiv.org/abs/2410.03857) [find](https://scholar.google.com/scholar?q=Tianyu+Wu%2C+Lingrui+Mei%2C+Ruibin+Yuan%2C+Lujun+Li%2C+Wei+Xue%2C+and+Yike+Guo%2E+2024%2E+You+know+what+i%E2%80%99m+saying%3A+Jailbreak+attack+via+implicit+reference%2E+arXiv+preprint+arXiv%3A2410%2E03857%2E+%E2%80%94+%5BarXiv%3A2410%2E03857%5D%28htt)
131. Zhaomin Wu, Haodong Zhao, Ziyang Wang, Jizhou Guo, Qian Wang, and Bingsheng He. 2025b. Llm dna: Tracing model evolution via functional representations. arXiv preprint arXiv:2509.24496. — [arXiv:2509.24496](https://arxiv.org/abs/2509.24496) [find](https://scholar.google.com/scholar?q=Zhaomin+Wu%2C+Haodong+Zhao%2C+Ziyang+Wang%2C+Jizhou+Guo%2C+Qian+Wang%2C+and+Bingsheng+He%2E+2025b%2E+Llm+dna%3A+Tracing+model+evolution+via+functional+representations%2E+arXiv+preprint+arXiv%3A2509%2E24496%2E+%E2%80%94+%5BarXiv%3A2509%2E2)
132. Magdalena Wysocka, Oskar Wysocki, Maxime Delmas, Vincent Mutel, and Andre Freitas. 2023. Large language models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery. arXiv. [find](https://scholar.google.com/scholar?q=Magdalena+Wysocka%2C+Oskar+Wysocki%2C+Maxime+Delmas%2C+Vincent+Mutel%2C+and+Andre+Freitas%2E+2023%2E+Large+language+models%2C+scientific+knowledge+and+factuality%3A+A+systematic+analysis+in+antibiotic+discovery%2E+arXi)
133. Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023. Adaptive chameleon or stubborn sloth: Unraveling the behavior of large language models in knowledge conflicts. arXiv. [find](https://scholar.google.com/scholar?q=Jian+Xie%2C+Kai+Zhang%2C+Jiangjie+Chen%2C+Renze+Lou%2C+and+Yu+Su%2E+2023%2E+Adaptive+chameleon+or+stubborn+sloth%3A+Unraveling+the+behavior+of+large+language+models+in+knowledge+conflicts%2E+arXiv%2E)
134. Rongwu Xu, Brian S Lin, Shujian Yang, Tianqi Zhang, Weiyan Shi, Tianwei Zhang, Zhixuan Fang, Wei Xu, and Han Qiu. 2023. The earth is flat because...: Investigating llms’ belief towards misinformation via persuasive conversation. arXiv preprint arXiv:2312.09085. [find](https://scholar.google.com/scholar?q=Rongwu+Xu%2C+Brian+S+Lin%2C+Shujian+Yang%2C+Tianqi+Zhang%2C+Weiyan+Shi%2C+Tianwei+Zhang%2C+Zhixuan+Fang%2C+Wei+Xu%2C+and+Han+Qiu%2E+2023%2E+The+earth+is+flat+because%2E%2E%2E%3A+Investigating+llms%E2%80%99+belief+towards+misinformation+)
135. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). arXiv preprint arXiv:2505.09388.
136. Xianjun Yang, Xiao Wang, Qi Zhang, Linda R. Petzold, William Yang Wang, Xun Zhao, and Dahua Lin. 2023. Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models. arxiv. [find](https://scholar.google.com/scholar?q=Xianjun+Yang%2C+Xiao+Wang%2C+Qi+Zhang%2C+Linda+R%2E+Petzold%2C+William+Yang+Wang%2C+Xun+Zhao%2C+and+Dahua+Lin%2E+2023%2E+Shadow+Alignment%3A+The+Ease+of+Subverting+Safely%2DAligned+Language+Models%2E+arxiv%2E)
137. Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang. 2024. [A survey on large language model (llm) security and privacy: The good, the bad, and the ugly](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/). High-Confidence Computing, 4(2):100211.
138. Haoran Ye, Jing Jin, Yuhang Xie, Xin Zhang, and Guojie Song. 2026. Large language model psychometrics: A systematic review of evaluation, validation, and enhancement. Preprint, arXiv:2505.08245. — [arXiv:2505.08245](https://arxiv.org/abs/2505.08245) [find](https://scholar.google.com/scholar?q=Haoran+Ye%2C+Jing+Jin%2C+Yuhang+Xie%2C+Xin+Zhang%2C+and+Guojie+Song%2E+2026%2E+Large+language+model+psychometrics%3A+A+systematic+review+of+evaluation%2C+validation%2C+and+enhancement%2E+Preprint%2C+arXiv%3A2505%2E08245%2E+%E2%80%94+%5Bar)
139. Haoran Ye, Yuhang Xie, Yuanyi Ren, Hanjun Fang, Xin Zhang, and Guojie Song. 2025a. Measuring human and ai values based on generative psychometrics with large language models. Preprint, arXiv:2409.12106. — [arXiv:2409.12106](https://arxiv.org/abs/2409.12106) [find](https://scholar.google.com/scholar?q=Haoran+Ye%2C+Yuhang+Xie%2C+Yuanyi+Ren%2C+Hanjun+Fang%2C+Xin+Zhang%2C+and+Guojie+Song%2E+2025a%2E+Measuring+human+and+ai+values+based+on+generative+psychometrics+with+large+language+models%2E+Preprint%2C+arXiv%3A2409%2E1210)
140. Haoran Ye, Tianze Zhang, Yuhang Xie, Liyuan Zhang, Yuanyi Ren, Xin Zhang, and Guojie Song. 2025b. Generative psycho-lexical approach for constructing value systems in large language models. Preprint, arXiv:2502.02444. [find](https://scholar.google.com/scholar?q=Haoran+Ye%2C+Tianze+Zhang%2C+Yuhang+Xie%2C+Liyuan+Zhang%2C+Yuanyi+Ren%2C+Xin+Zhang%2C+and+Guojie+Song%2E+2025b%2E+Generative+psycho%2Dlexical+approach+for+constructing+value+systems+in+large+language+models%2E+Preprint%2C+)
141. Zixuan Yu, Zhenheng Tang, Tongliang Liu, Chengqi Zhang, Xiaowen Chu, and Bo Han. 2026. Rethinking deep research from the perspective of web content distribution matching. arXiv preprint arXiv:2603.07241. — [arXiv:2603.07241](https://arxiv.org/abs/2603.07241) [find](https://scholar.google.com/scholar?q=Zixuan+Yu%2C+Zhenheng+Tang%2C+Tongliang+Liu%2C+Chengqi+Zhang%2C+Xiaowen+Chu%2C+and+Bo+Han%2E+2026%2E+Rethinking+deep+research+from+the+perspective+of+web+content+distribution+matching%2E+arXiv+preprint+arXiv%3A2603%2E072)
142. Yi Zeng, Yu Yang, Andy Zhou, Jeffrey Ziwei Tan, Yuheng Tu, Yifan Mai, Kevin Klyman, Minzhou Pan, Ruoxi Jia, Dawn Song, and 1 others. 2024. [find](https://scholar.google.com/scholar?q=Yi+Zeng%2C+Yu+Yang%2C+Andy+Zhou%2C+Jeffrey+Ziwei+Tan%2C+Yuheng+Tu%2C+Yifan+Mai%2C+Kevin+Klyman%2C+Minzhou+Pan%2C+Ruoxi+Jia%2C+Dawn+Song%2C+and+1+others%2E+2024%2E)
143. Air-bench 2024: A safety benchmark based on risk categories from regulations and policies. arXiv preprint arXiv:2407.17436. [find](https://scholar.google.com/scholar?q=Air%2Dbench+2024%3A+A+safety+benchmark+based+on+risk+categories+from+regulations+and+policies%2E+arXiv+preprint+arXiv%3A2407%2E17436%2E)
144. Zaibin Zhang, Yongting Zhang, Lijun Li, Hongzhi Gao, Lijun Wang, Huchuan Lu, Feng Zhao, Yu Qiao, and Jing Shao. 2024. PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety. arxiv. [find](https://scholar.google.com/scholar?q=Zaibin+Zhang%2C+Yongting+Zhang%2C+Lijun+Li%2C+Hongzhi+Gao%2C+Lijun+Wang%2C+Huchuan+Lu%2C+Feng+Zhao%2C+Yu+Qiao%2C+and+Jing+Shao%2E+2024%2E+PsySafe%3A+A+Comprehensive+Framework+for+Psychological%2Dbased+Attack%2C+Defense%2C+and+Ev)
145. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. [Calibrate before use: Improving few-shot performance of language models](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/). In International Conference on Machine Learning, pages 12697–12706. PMLR.
146. Shen Zheng, Jie Huang, and Kevin Chen-Chuan Chang. 2023. Why does chatgpt fall short in providing truthful answers. arXiv. [find](https://scholar.google.com/scholar?q=Shen+Zheng%2C+Jie+Huang%2C+and+Kevin+Chen%2DChuan+Chang%2E+2023%2E+Why+does+chatgpt+fall+short+in+providing+truthful+answers%2E+arXiv%2E)
147. Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021. [Factual probing is MASK: Learning vs. learning to recall](https://md.research.elamur.ai/free/en/papers/zhong-2021-factual-probing-is-mask-learning/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5017–5033, Online. Association for Co…
148. Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023a. [Instruction-following evaluation for large language models](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/). arXiv preprint arXiv:2311.07911.
149. Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023b. [Context-faithful prompting for large language models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/). arXiv.
150. Yukai Zhou and Wenjie Wang. 2024. Don’t Say No: Jailbreaking LLM by Suppressing Refusal. arxiv. [find](https://scholar.google.com/scholar?q=Yukai+Zhou+and+Wenjie+Wang%2E+2024%2E+Don%E2%80%99t+Say+No%3A+Jailbreaking+LLM+by+Suppressing+Refusal%2E+arxiv%2E)
151. Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun. 2023. AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models. arxiv. [find](https://scholar.google.com/scholar?q=Sicheng+Zhu%2C+Ruiyi+Zhang%2C+Bang+An%2C+Gang+Wu%2C+Joe+Barrow%2C+Zichao+Wang%2C+Furong+Huang%2C+Ani+Nenkova%2C+and+Tong+Sun%2E+2023%2E+AutoDAN%3A+Interpretable+Gradient%2DBased+Adversarial+Attacks+on+Large+Language+Models%2E+)
152. Yuanbing Zhu, Zhenheng Tang, Xiang Liu, Ang Li, Bo Li, Xiaowen Chu, and Bo Han. 2025. OracleKV: Oracle guidance for question-independent KV cache compression. In ICML 2025 Workshop on Long-Context Foundation Models. [find](https://scholar.google.com/scholar?q=Yuanbing+Zhu%2C+Zhenheng+Tang%2C+Xiang+Liu%2C+Ang+Li%2C+Bo+Li%2C+Xiaowen+Chu%2C+and+Bo+Han%2E+2025%2E+OracleKV%3A+Oracle+guidance+for+question%2Dindependent+KV+cache+compression%2E+In+ICML+2025+Workshop+on+Long%2DContext+Fou)
153. Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson. 2023. [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/). arxiv.

*Updated 1 November 2026*