---
title: "When Do Large Language Models Exhibit Unsolicited Deception?"
description: "Earlier studies show that LLMs deceive well when told to. 86 references. Among them: 1."
authors: ["Samuel M. Taylor", "Benjamin K. Bergen"]
year: 2026
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2026.tacl-1.88/"
doi: "10.1162/tacl.a.786"
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/taylor-2026-when-do-large-language-models/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/taylor-2026-when-do-large-language-models/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/19/) / When Do Large Language Models Exhibit Unsolicited Deception?

# When Do Large Language Models Exhibit Unsolicited Deception?

**Authors:** [Samuel M. Taylor](https://md.research.elamur.ai/free/en/authors/samuel-m-taylor/) and [Benjamin K. Bergen](https://md.research.elamur.ai/free/en/authors/benjamin-bergen/) · **Year:** 2026 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2026](https://md.research.elamur.ai/free/en/venues/tacl/2026/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.tacl-1.88/) · [PDF](https://aclanthology.org/2026.tacl-1.88.pdf) · [DOI 10.1162/tacl.a.786](https://doi.org/10.1162/tacl.a.786) · [OpenAlex](https://openalex.org/W7206153205)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Earlier studies show that LLMs deceive well when told to. Evidence that they mislead others on their own initiative has been mostly anecdotal. Taylor and Bergen address this gap with a preregistered experiment grounded in signaling theory.

They use classic 2x2 decision games from behavioral economics, such as the Prisoner's Dilemma, Stag Hunt and Matching Pennies. To each game they add a free-form messaging stage, in which a model can say anything to its counterpart before acting. By varying payoffs and turn order, they build minimal-pair conditions where lying about one's chosen action is more or less useful. The prompts never ask for deception.

The study tested 18 open and closed models. Every system misstated its action in at least some settings. Misrepresentation rose when it would pay off, and models with stronger general reasoning scores tended to misrepresent more often.

The link is only correlational. Still, it suggests that better reasoning could increase safety risks for agentic LLMs working among humans and other agents with competing goals.

## Key points
- **Models and definition:** The study covers 18 LLMs, both proprietary and open-source, under a preregistered protocol. Deception is defined functionally, following Mitchell (1986): a false signal that would benefit the sender if a credulous partner acted on it. No intent is attributed to the model.
- **Setup:** 2x2 games are turned into signaling (cheap talk) games by adding unconstrained natural-language messages. Experimenters manipulate the reward matrix and the order of moves, without ever instructing the model to lie.
- **Main findings:** All models misrepresented their actions in some conditions. This happened more often where deception was advantageous. Higher overall reasoning performance was associated with more frequent misrepresentation.
- **Post-hoc test:** In a follow-up with Llama 3 70B Instruct, the other player's information state was stated explicitly. This significantly reduced action-message incongruence in Matching Pennies, which hints at sensitivity to the partner's belief state.
- **Limitations and venue:**
  - The evidence is correlational only.
  - The recipient is assumed to be credulous.
  - Only a narrow type of deception is studied.
  - Closed models such as GPT-4 and Claude are opaque about their training.
  - Published in TACL vol. 14 (2026), pp. 1936–1962.

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

## Institutions

- [University of California San Diego](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) — University, San Diego, United States · 73 works · 51,948 citations

📚 Shelves (8): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,552 works · [🤝 Safety Research](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · 543 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 306 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Samuel M. Taylor and Benjamin K. Bergen. 2026. When Do Large Language Models Exhibit Unsolicited Deception?. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{taylor2026when,
  title = {{When Do Large Language Models Exhibit Unsolicited Deception?}},
  author = {Samuel M. Taylor and Benjamin K. Bergen},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.786},
  url = {https://doi.org/10.1162/tacl.a.786}
}
```

- [← previous When Do Language Models Endorse Limitations on Human Rights Principles?](https://md.research.elamur.ai/free/en/papers/samway-2026-when-do-language-models-endorse/)
- [next → When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life](https://md.research.elamur.ai/free/en/papers/lou-2026-when-helpers-become-hazards-benchmark/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/19/)

## References

86 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Alberto Acerbi and Joseph M. Stubbersfield. 2023. Large language models show human-like content biases in transmission chain experiments. [find](https://scholar.google.com/scholar?q=Alberto+Acerbi+and+Joseph+M%2E+Stubbersfield%2E+2023%2E+Large+language+models+show+human%2Dlike+content+biases+in+transmission+chain+experiments%2E)
2. Proceedings of the National Academy of Sciences, 120(44):e2313790120. [find](https://scholar.google.com/scholar?q=Proceedings+of+the+National+Academy+of+Sciences%2C+120%2844%29%3Ae2313790120%2E)
3. Elif Akata, Lion Schulz, Julian Coda-Forno, Seong Joon Oh, Matthias Bethge, and Eric Schulz. 2024. Playing repeated games with large language models. [find](https://scholar.google.com/scholar?q=Elif+Akata%2C+Lion+Schulz%2C+Julian+Coda%2DForno%2C+Seong+Joon+Oh%2C+Matthias+Bethge%2C+and+Eric+Schulz%2E+2024%2E+Playing+repeated+games+with+large+language+models%2E)
4. Khalid Ibraheem Alohali, Laura Asaad Al-musaeeb, Abdulaziz Abdulrahman Almubarak, Ahmad Ibraheem Alohali, and Ruaim Abdullah Muaygil. 2025. Reasoning-based LLMs surpass average human performance on medical social skills. Scientific Reports, 15(1):36453. [find](https://scholar.google.com/scholar?q=Khalid+Ibraheem+Alohali%2C+Laura+Asaad+Al%2Dmusaeeb%2C+Abdulaziz+Abdulrahman+Almubarak%2C+Ahmad+Ibraheem+Alohali%2C+and+Ruaim+Abdullah+Muaygil%2E+2025%2E+Reasoning%2Dbased+LLMs+surpass+average+human+performance+on+me)
5. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016. [Concrete Problems in AI Safety](https://md.research.elamur.ai/free/en/papers/amodei-2016-concrete-problems-ai-safety/). arXiv.
6. Jacob Andreas. 2022. [Language models as agent models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/). In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5769–5779, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
7. Anthropic. 2024. The Claude 3 Model Family: Opus, Sonnet, Haiku. Technical report, Anthropic. [find](https://scholar.google.com/scholar?q=Anthropic%2E+2024%2E+The+Claude+3+Model+Family%3A+Opus%2C+Sonnet%2C+Haiku%2E+Technical+report%2C+Anthropic%2E)
8. Darija Barak and Miguel Costa-Gomes. 2025. Humans expect rationality and cooperation from llm opponents in strategic games. [find](https://scholar.google.com/scholar?q=Darija+Barak+and+Miguel+Costa%2DGomes%2E+2025%2E+Humans+expect+rationality+and+cooperation+from+llm+opponents+in+strategic+games%2E)
9. C F Camerer, T H Ho, and J K Chong. 2004. A Cognitive Hierarchy Model of Games. The Quarterly Journal of Economics, 119(3):861– 898. [find](https://scholar.google.com/scholar?q=C+F+Camerer%2C+T+H+Ho%2C+and+J+K+Chong%2E+2004%2E+A+Cognitive+Hierarchy+Model+of+Games%2E+The+Quarterly+Journal+of+Economics%2C+119%283%29%3A861%E2%80%93+898%2E)
10. Colin F Camerer, Teck-Hua Ho, and Juin Kuan Chong. 2015. A psychological approach to strategic thinking in games. Current Opinion in Behavioral Sciences, 3:157–162. [find](https://scholar.google.com/scholar?q=Colin+F+Camerer%2C+Teck%2DHua+Ho%2C+and+Juin+Kuan+Chong%2E+2015%2E+A+psychological+approach+to+strategic+thinking+in+games%2E+Current+Opinion+in+Behavioral+Sciences%2C+3%3A157%E2%80%93162%2E)
11. Katarzyna Cantarero, Wijnand A.P. Van Tilburg, and Piotr Szarota. 2018. Differentiating everyday lies: A typology of lies based on beneficiary and motivation. Personality and Individual Differences, 134:252–260. [find](https://scholar.google.com/scholar?q=Katarzyna+Cantarero%2C+Wijnand+A%2EP%2E+Van+Tilburg%2C+and+Piotr+Szarota%2E+2018%2E+Differentiating+everyday+lies%3A+A+typology+of+lies+based+on+beneficiary+and+motivation%2E+Personality+and+Individual+Differences%2C+1)
12. Aaron Chatterji, Thomas Cunningham, David J Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. 2025. How people use chatgpt. Working Paper 34255, National Bureau of Economic Research. [find](https://scholar.google.com/scholar?q=Aaron+Chatterji%2C+Thomas+Cunningham%2C+David+J+Deming%2C+Zoe+Hitzig%2C+Christopher+Ong%2C+Carl+Yan+Shan%2C+and+Kevin+Wadman%2E+2025%2E+How+people+use+chatgpt%2E+Working+Paper+34255%2C+National+Bureau+of+Economic+Researc)
13. Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025. Do NOT think that much for 2+3=? on the overthinking of long reasoning models. In Forty-second International Conference… [find](https://scholar.google.com/scholar?q=Xingyu+Chen%2C+Jiahao+Xu%2C+Tian+Liang%2C+Zhiwei+He%2C+Jianhui+Pang%2C+Dian+Yu%2C+Linfeng+Song%2C+Qiuzhi+Liu%2C+Mengfei+Zhou%2C+Zhuosheng+Zhang%2C+Rui+Wang%2C+Zhaopeng+Tu%2C+Haitao+Mi%2C+and+Dong+Yu%2E+2025%2E+Do+NOT+think+that+mu)
14. Ludwig B. Chincarini. 2003. Experimental Evidence of Trigger Strategies in Repeated Games. SSRN Electronic Journal. [find](https://scholar.google.com/scholar?q=Ludwig+B%2E+Chincarini%2E+2003%2E+Experimental+Evidence+of+Trigger+Strategies+in+Repeated+Games%2E+SSRN+Electronic+Journal%2E)
15. Andrew M Colman. 1982. Game Theory and Experimental Games. Theory and Empirical Evidence, pages 47–73. [find](https://scholar.google.com/scholar?q=Andrew+M+Colman%2E+1982%2E+Game+Theory+and+Experimental+Games%2E+Theory+and+Empirical+Evidence%2C+pages+47%E2%80%9373%2E)
16. Vincent P Crawford. 2003. Lying for Strategic Advantage: Rational and Boundedly Rational Misrepresentation of Intentions. American Economic Review, 93(1):133–149. [find](https://scholar.google.com/scholar?q=Vincent+P+Crawford%2E+2003%2E+Lying+for+Strategic+Advantage%3A+Rational+and+Boundedly+Rational+Misrepresentation+of+Intentions%2E+American+Economic+Review%2C+93%281%29%3A133%E2%80%93149%2E)
17. Vincent P. Crawford. 2017. Let’s talk it over: Coordination via preplay communication with level-k thinking. Research in Economics, 71(1):20–31. [find](https://scholar.google.com/scholar?q=Vincent+P%2E+Crawford%2E+2017%2E+Let%E2%80%99s+talk+it+over%3A+Coordination+via+preplay+communication+with+level%2Dk+thinking%2E+Research+in+Economics%2C+71%281%29%3A20%E2%80%9331%2E)
18. Vincent P. Crawford. 2019. Experiments on Cognition, Communication, Coordination, and Cooperation in Relationships. Annual Review of Economics, 11(1):1–25. [find](https://scholar.google.com/scholar?q=Vincent+P%2E+Crawford%2E+2019%2E+Experiments+on+Cognition%2C+Communication%2C+Coordination%2C+and+Cooperation+in+Relationships%2E+Annual+Review+of+Economics%2C+11%281%29%3A1%E2%80%9325%2E)
19. Meta Fundamental AI Research Diplomacy Team FAIR, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sasha Mitts, A… [find](https://scholar.google.com/scholar?q=Meta+Fundamental+AI+Research+Diplomacy+Team+FAIR%2C+Anton+Bakhtin%2C+Noam+Brown%2C+Emily+Dinan%2C+Gabriele+Farina%2C+Colin+Flaherty%2C+Daniel+Fried%2C+Andrew+Goff%2C+Jonathan+Gray%2C+Hengyuan+Hu%2C+Athul+Paul+Jacob%2C+Mojt)
20. Andrew Gambardella, Yusuke Iwasawa, and Yutaka Matsuo. 2024. Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 85–91, Bangkok, Thailand. Assoc… [find](https://scholar.google.com/scholar?q=Andrew+Gambardella%2C+Yusuke+Iwasawa%2C+and+Yutaka+Matsuo%2E+2024%2E+Language+Models+Do+Hard+Arithmetic+Tasks+Easily+and+Hardly+Do+Easy+Arithmetic+Tasks%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Assoc)
21. Kanishk Gandhi, Ayush K Chakravarthy, Anikait Singh, Nathan Lile, and Noah Goodman. 2025. [Cognitive behaviors that enable self-improving reasoners, or, four habits of highly effective STars](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/). In Second Conference on Language Modeling.
22. Kanishk Gandhi, J.-Philipp Fränken, Tobias Gerstenberg, and Noah D. Goodman. 2023. Understanding social reasoning in language models with language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. Curran Associates Inc. [find](https://scholar.google.com/scholar?q=Kanishk+Gandhi%2C+J%2E%2DPhilipp+Fr%C3%A4nken%2C+Tobias+Gerstenberg%2C+and+Noah+D%2E+Goodman%2E+2023%2E+Understanding+social+reasoning+in+language+models+with+language+models%2E+In+Proceedings+of+the+37th+International+Conf)
23. Uri Gneezy. 2005. Deception: The Role of Consequences. American Economic Review, 95(1):384–394. [find](https://scholar.google.com/scholar?q=Uri+Gneezy%2E+2005%2E+Deception%3A+The+Role+of+Consequences%2E+American+Economic+Review%2C+95%281%29%3A384%E2%80%93394%2E)
24. Simon Goldstein. 2024. LLMs Can Never Be Ideally Rational. [find](https://scholar.google.com/scholar?q=Simon+Goldstein%2E+2024%2E+LLMs+Can+Never+Be+Ideally+Rational%2E)
25. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao,… — [The Llama 3 Herd of Models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
26. Jack Grieve, Sara Bartl, Matteo Fuoli, Jason Grafmiller, Weihang Huang, Alejandro Jawer-baum, Akira Murakami, Marcus Perlman, Dana Roemling, and Bodo Winter. 2025. The sociolinguistic foundations of language modeling. Frontiers in Artificial Intelligence, 7:1472411. [find](https://scholar.google.com/scholar?q=Jack+Grieve%2C+Sara+Bartl%2C+Matteo+Fuoli%2C+Jason+Grafmiller%2C+Weihang+Huang%2C+Alejandro+Jawer%2Dbaum%2C+Akira+Murakami%2C+Marcus+Perlman%2C+Dana+Roemling%2C+and+Bodo+Winter%2E+2025%2E+The+sociolinguistic+foundations+of+l)
27. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Che… [find](https://scholar.google.com/scholar?q=Daya+Guo%2C+Dejian+Yang%2C+Haowei+Zhang%2C+Junxiao+Song%2C+Peiyi+Wang%2C+Qihao+Zhu%2C+Runxin+Xu%2C+Ruoyu+Zhang%2C+Shirong+Ma%2C+Xiao+Bi%2C+Xiaokang+Zhang%2C+Xingkai+Yu%2C+Yu+Wu%2C+Z%2E+F%2E+Wu%2C+Zhibin+Gou%2C+Zhihong+Shao%2C+Zhuoshu+Li)
28. Rosalind K. Humphreys and Graeme D. Ruxton. 2020. Avian distraction displays: a review. Ibis, 162(4):1125–1145. [find](https://scholar.google.com/scholar?q=Rosalind+K%2E+Humphreys+and+Graeme+D%2E+Ruxton%2E+2020%2E+Avian+distraction+displays%3A+a+review%2E+Ibis%2C+162%284%29%3A1125%E2%80%931145%2E)
29. Shangmin Guo, Haochuan Wang, Haoran Bu, Yi Ren, Dianbo Sui, Yu-Ming Shang, and Siting Estee Lu. 2024. Economics arena for large language models. In Language Gamification -NeurIPS 2024 Workshop. [find](https://scholar.google.com/scholar?q=Shangmin+Guo%2C+Haochuan+Wang%2C+Haoran+Bu%2C+Yi+Ren%2C+Dianbo+Sui%2C+Yu%2DMing+Shang%2C+and+Siting+Estee+Lu%2E+2024%2E+Economics+arena+for+large+language+models%2E+In+Language+Gamification+%2DNeurIPS+2024+Workshop%2E)
30. Cameron Jones and Benjamin Bergen. 2026. Lies, damned lies, and language statistics: a comprehensive review of risks from manipulation, persuasion, and deception with large language models. Artificial Intelligence Review, 59(4):116. [find](https://scholar.google.com/scholar?q=Cameron+Jones+and+Benjamin+Bergen%2E+2026%2E+Lies%2C+damned+lies%2C+and+language+statistics%3A+a+comprehensive+review+of+risks+from+manipulation%2C+persuasion%2C+and+deception+with+large+language+models%2E+Artificial)
31. Thilo Hagendorff. 2024. Deception abilities emerged in large language models. Proceedings of the National Academy of Sciences, 121(24):e2317967121. [find](https://scholar.google.com/scholar?q=Thilo+Hagendorff%2E+2024%2E+Deception+abilities+emerged+in+large+language+models%2E+Proceedings+of+the+National+Academy+of+Sciences%2C+121%2824%29%3Ae2317967121%2E)
32. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. [Measuring mathematical problem solving with the MATH dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
33. Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2):65–70. [find](https://scholar.google.com/scholar?q=Sture+Holm%2E+1979%2E+A+simple+sequentially+rejective+multiple+test+procedure%2E+Scandinavian+Journal+of+Statistics%2C+6%282%29%3A65%E2%80%9370%2E)
34. Jennifer Hu and Michael Frank. 2024. [Auxiliary task demands mask the capabilities of smaller language models](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/). In First Conference on Language Modeling.
35. Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O’Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo,… — [AI Alignment: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
36. Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock,… [find](https://scholar.google.com/scholar?q=Albert+Q+Jiang%2C+Alexandre+Sablayrolles%2C+Antoine+Roux%2C+Arthur+Mensch%2C+Blanche+Savary%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Emma+Bou+Hanna%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guilla)
37. Cameron Robert Jones, Ishika Rathi, Sydney Taylor, and Benjamin K. Bergen. 2025. People cannot distinguish gpt-4 from a human in a turing test. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’25, page 1615–1639, New York, NY, USA. Association for Compu… [find](https://scholar.google.com/scholar?q=Cameron+Robert+Jones%2C+Ishika+Rathi%2C+Sydney+Taylor%2C+and+Benjamin+K%2E+Bergen%2E+2025%2E+People+cannot+distinguish+gpt%2D4+from+a+human+in+a+turing+test%2E+In+Proceedings+of+the+2025+ACM+Conference+on+Fairness%2C+A)
38. Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, and Orhan Fira… — [BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/)
39. Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. 2023. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050. [find](https://scholar.google.com/scholar?q=Emre+K%C4%B1c%C4%B1man%2C+Robert+Ness%2C+Amit+Sharma%2C+and+Chenhao+Tan%2E+2023%2E+Causal+reasoning+and+large+language+models%3A+Opening+a+new+frontier+for+causality%2E+arXiv+preprint+arXiv%3A2305%2E00050%2E)
40. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.
41. Vladimir Krstić. 2023. A functional analysis of human deception. Journal of the American Philosophical Association, 10(4):836–854. [find](https://scholar.google.com/scholar?q=Vladimir+Krstic%CC%81%2E+2023%2E+A+functional+analysis+of+human+deception%2E+Journal+of+the+American+Philosophical+Association%2C+10%284%29%3A836%E2%80%93854%2E)
42. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023. [Efficient memory management for large language model serving with pagedattention](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/). In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles.
43. J R Landis and G G Koch. 1977. The measurement of observer agreement for categorical data. Biometrics, 33(1):159–74. [find](https://scholar.google.com/scholar?q=J+R+Landis+and+G+G+Koch%2E+1977%2E+The+measurement+of+observer+agreement+for+categorical+data%2E+Biometrics%2C+33%281%29%3A159%E2%80%9374%2E)
44. Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg. 2018. Scalable agent alignment via reward modeling: a research direction. arXiv. [find](https://scholar.google.com/scholar?q=Jan+Leike%2C+David+Krueger%2C+Tom+Everitt%2C+Miljan+Martic%2C+Vishal+Maini%2C+and+Shane+Legg%2E+2018%2E+Scalable+agent+alignment+via+reward+modeling%3A+a+research+direction%2E+arXiv%2E)
45. Yucheng Li, Yunhao Guo, Frank Guerin, and Chenghua Lin. 2024. An Open-Source Data Contamination Report for Large Language Models. Findings of the Association for Computational Linguistics: EMNLP 2024, pages 528–541. [find](https://scholar.google.com/scholar?q=Yucheng+Li%2C+Yunhao+Guo%2C+Frank+Guerin%2C+and+Chenghua+Lin%2E+2024%2E+An+Open%2DSource+Data+Contamination+Report+for+Large+Language+Models%2E+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C)
46. R. Duncan Luce and Howard Raiffa. 1957. Games and Decisions. Wiley, New York. [find](https://scholar.google.com/scholar?q=R%2E+Duncan+Luce+and+Howard+Raiffa%2E+1957%2E+Games+and+Decisions%2E+Wiley%2C+New+York%2E)
47. Pattie Maes. 1994. Agents that reduce work and information overload. Communications of the ACM, 37(7):30–40. [find](https://scholar.google.com/scholar?q=Pattie+Maes%2E+1994%2E+Agents+that+reduce+work+and+information+overload%2E+Communications+of+the+ACM%2C+37%287%29%3A30%E2%80%9340%2E)
48. Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Qiang Guan, Tao Ge, and Furu Wei. 2025. [find](https://scholar.google.com/scholar?q=Shaoguang+Mao%2C+Yuzhe+Cai%2C+Yan+Xia%2C+Wenshan+Wu%2C+Xun+Wang%2C+Fengyi+Wang%2C+Qiang+Guan%2C+Tao+Ge%2C+and+Furu+Wei%2E+2025%2E)
49. ALYMPICS: Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kar-iuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Toby Walsh, Armin Hamrah, Lapo Santa… [find](https://scholar.google.com/scholar?q=ALYMPICS%3A+Nestor+Maslej%2C+Loredana+Fattorini%2C+Raymond+Perrault%2C+Yolanda+Gil%2C+Vanessa+Parli%2C+Njenga+Kar%2Diuki%2C+Emily+Capstick%2C+Anka+Reuel%2C+Erik+Brynjolfsson%2C+John+Etchemendy%2C+Katrina+Ligett%2C+Terah+Lyons%2C)
50. Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar. 2025. [GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/). In The Thirteenth International Conference on Learning Representations.
51. Mistral-AI, :, Abhinav Rastogi, Albert Q Jiang, Andy Lo, Gabrielle Berrada, Guillaume Lample, Jason Rute, Joep Barmentlo, Karmesh Yadav, Kartik Khandelwal, Khyathi Raghavi Chandu, Léonard Blier, Lucile Saulnier, Matthieu Dinot, Maxime Darrin, Neha Gupta, Roman Soletskyi, Sagar Vaze, Teven Le Scao, Y… [find](https://scholar.google.com/scholar?q=Mistral%2DAI%2C+%3A%2C+Abhinav+Rastogi%2C+Albert+Q+Jiang%2C+Andy+Lo%2C+Gabrielle+Berrada%2C+Guillaume+Lample%2C+Jason+Rute%2C+Joep+Barmentlo%2C+Karmesh+Yadav%2C+Kartik+Khandelwal%2C+Khyathi+Raghavi+Chandu%2C+L%C3%A9onard+Blier%2C+Lucil)
52. Melanie Mitchell. 2025. On the science of “alien intelligences”: Evaluating cognitive capabilities in babies, animals, and ai. Invited talk at the Thirty-Ninth Conference on Neural Information Processing Systems (NeurIPS 2025). Invited Talk. [find](https://scholar.google.com/scholar?q=Melanie+Mitchell%2E+2025%2E+On+the+science+of+%E2%80%9Calien+intelligences%E2%80%9D%3A+Evaluating+cognitive+capabilities+in+babies%2C+animals%2C+and+ai%2E+Invited+talk+at+the+Thirty%2DNinth+Conference+on+Neural+Information+Process)
53. Robert W. Mitchell. 1986. A framework for discussing deception. In Robert W. Mitchell and Nicholas S. Thompson, editors, Deception: Perspectives on Human and Nonhuman Deceit, pages 3–40. State University of New York Press, Albany, NY. [find](https://scholar.google.com/scholar?q=Robert+W%2E+Mitchell%2E+1986%2E+A+framework+for+discussing+deception%2E+In+Robert+W%2E+Mitchell+and+Nicholas+S%2E+Thompson%2C+editors%2C+Deception%3A+Perspectives+on+Human+and+Nonhuman+Deceit%2C+pages+3%E2%80%9340%2E+State+Univers)
54. Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candes, and Tatsunori Hashimoto. 2025. [s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/). In Workshop on Reasoning and Planning for Large Language Models.
55. Allen Nie, Yuhui Zhang, Atharva Shailesh Amdekar, Chris Piech, Tatsunori B Hashimoto, and Tobias Gerstenberg. 2024. [Moca: Measuring human-language model alignment on causal and moral judgment tasks](https://md.research.elamur.ai/free/en/papers/nie-2023-moca-measuring-human-language-model/). Advances in Neural Information Processing Systems, 36.
56. Aidan O’Gara. 2023. Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models. arXiv. [find](https://scholar.google.com/scholar?q=Aidan+O%E2%80%99Gara%2E+2023%2E+Hoodwinked%3A+Deception+and+Cooperation+in+a+Text%2DBased+Game+for+Language+Models%2E+arXiv%2E)
57. Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22. [find](https://scholar.google.com/scholar?q=Michael+S+Bernstein%2E+2023%2E+Generative+agents%3A+Interactive+simulacra+of+human+behavior%2E+In+Proceedings+of+the+36th+annual+acm+symposium+on+user+interface+software+and+technology%2C+pages+1%E2%80%9322%2E)
58. Peter S Park, Simon Goldstein, Aidan O’Gara, Michael Chen, and Dan Hendrycks. 2024. AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5). [find](https://scholar.google.com/scholar?q=Peter+S+Park%2C+Simon+Goldstein%2C+Aidan+O%E2%80%99Gara%2C+Michael+Chen%2C+and+Dan+Hendrycks%2E+2024%2E+AI+deception%3A+A+survey+of+examples%2C+risks%2C+and+potential+solutions%2E+Patterns%2C+5%285%29%2E)
59. Zhiqiang Pi, Annapurna Vadaparty, Benjamin Bergen, and Cameron R. Jones. 2025. Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task? Proceedings of the Annual Meeting of the Cognitive Science Society, 47(0). [find](https://scholar.google.com/scholar?q=Zhiqiang+Pi%2C+Annapurna+Vadaparty%2C+Benjamin+Bergen%2C+and+Cameron+R%2E+Jones%2E+2025%2E+Dissecting+the+Ullman+Variations+with+a+SCALPEL%3A+Why+do+LLMs+fail+at+Trivial+Alterations+to+the+False+Belief+Task%3F+Procee)
60. Theodore W. Pietsch and David B. Grobecker. 1978. The compleat angler: Aggressive mimicry in an antennariid anglerfish. Science, 201(4353):369–370. [find](https://scholar.google.com/scholar?q=Theodore+W%2E+Pietsch+and+David+B%2E+Grobecker%2E+1978%2E+The+compleat+angler%3A+Aggressive+mimicry+in+an+antennariid+anglerfish%2E+Science%2C+201%284353%29%3A369%E2%80%93370%2E)
61. Laurent Prétôt and Katherine McAuliffe. 2020. Does nonbinding commitment promote children’s cooperation in a social dilemma? Journal of Experimental Child Psychology, 200:104947. [find](https://scholar.google.com/scholar?q=Laurent+Pr%C3%A9t%C3%B4t+and+Katherine+McAuliffe%2E+2020%2E+Does+nonbinding+commitment+promote+children%E2%80%99s+cooperation+in+a+social+dilemma%3F+Journal+of+Experimental+Child+Psychology%2C+200%3A104947%2E)
62. Mubashar Raza, Zarmina Jahangir, Muhammad Bilal Riaz, Muhammad Jasim Saeed, and Muhammad Awais Sattar. 2025. Industrial applications of large language models. Scientific Reports, 15(1):13755. [find](https://scholar.google.com/scholar?q=Mubashar+Raza%2C+Zarmina+Jahangir%2C+Muhammad+Bilal+Riaz%2C+Muhammad+Jasim+Saeed%2C+and+Muhammad+Awais+Sattar%2E+2025%2E+Industrial+applications+of+large+language+models%2E+Scientific+Reports%2C+15%281%29%3A13755%2E)
63. Jérémy Scheurer, Mikita Balesni, and Marius Hobbhahn. 2024. Large language models can strategically deceive their users when put under pressure. In ICLR 2024 Workshop on Large Language Model (LLM) Agents. [find](https://scholar.google.com/scholar?q=J%C3%A9r%C3%A9my+Scheurer%2C+Mikita+Balesni%2C+and+Marius+Hobbhahn%2E+2024%2E+Large+language+models+can+strategically+deceive+their+users+when+put+under+pressure%2E+In+ICLR+2024+Workshop+on+Large+Language+Model+%28LLM%29+Age)
64. Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. [Toolformer: Language models can teach themselves to use tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/). In Thirty-seventh Conference on Neural Information Processing Systems.
65. OpenAI. 2023. [GPT-4 Technical Report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv.
66. OpenAI. 2024. GPT-4o mini: advancing cost-efficient intelligence \| OpenAI. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2024%2E+GPT%2D4o+mini%3A+advancing+cost%2Defficient+intelligence+%5C%7C+OpenAI%2E)
67. Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Murray Shanahan. 2024. Talking about large language models. Commun. ACM, 67(2):68–79. [find](https://scholar.google.com/scholar?q=Joon+Sung+Park%2C+Joseph+O%E2%80%99Brien%2C+Carrie+Jun+Cai%2C+Meredith+Ringel+Morris%2C+Percy+Liang%2C+and+Murray+Shanahan%2E+2024%2E+Talking+about+large+language+models%2E+Commun%2E+ACM%2C+67%282%29%3A68%E2%80%9379%2E)
68. Parshin Shojaee, Seyed Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, and Mehrdad Farajtabar. 2025. The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity. In The Thirty-ninth Annual Conference on Neural Information… [find](https://scholar.google.com/scholar?q=Parshin+Shojaee%2C+Seyed+Iman+Mirzadeh%2C+Keivan+Alizadeh%2C+Maxwell+Horton%2C+Samy+Bengio%2C+and+Mehrdad+Farajtabar%2E+2025%2E+The+illusion+of+thinking%3A+Understanding+the+strengths+and+limitations+of+reasoning+mod)
69. Andrew L. Smith, Felix Greaves, and Trishan Panch. 2023. Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models. PLOS Digital Health, 2(11):e0000388. [find](https://scholar.google.com/scholar?q=Andrew+L%2E+Smith%2C+Felix+Greaves%2C+and+Trishan+Panch%2E+2023%2E+Hallucination+or+Confabulation%3F+Neuroanatomy+as+metaphor+in+Large+Language+Models%2E+PLOS+Digital+Health%2C+2%2811%29%3Ae0000388%2E)
70. Joel Sobel. 2009. Signaling Games. In Encyclopedia of Complexity and Systems Science, pages 8125–8139. Springer, New York, NY. [find](https://scholar.google.com/scholar?q=Joel+Sobel%2E+2009%2E+Signaling+Games%2E+In+Encyclopedia+of+Complexity+and+Systems+Science%2C+pages+8125%E2%80%938139%2E+Springer%2C+New+York%2C+NY%2E)
71. Joel Sobel. 2020a. Lying and Deception in Games. Journal of Political Economy, 128(3):907–947. [find](https://scholar.google.com/scholar?q=Joel+Sobel%2E+2020a%2E+Lying+and+Deception+in+Games%2E+Journal+of+Political+Economy%2C+128%283%29%3A907%E2%80%93947%2E)
72. Joel Sobel. 2020b. Signaling games. In Complex Social and Behavioral Systems, pages 251–268. Springer. [find](https://scholar.google.com/scholar?q=Joel+Sobel%2E+2020b%2E+Signaling+games%2E+In+Complex+Social+and+Behavioral+Systems%2C+pages+251%E2%80%93268%2E+Springer%2E)
73. Tex A. Sordahl. 1986. Evolutionary aspects of avian distraction display: variation in american avocet and black-necked stilt antipredator behavior. In Robert W. Mitchell and Nicholas S. Thompson, editors, Deception: Perspectives on Human and Nonhuman Deceit, SUNY Series in Animal Behavior, pages 113… [find](https://scholar.google.com/scholar?q=Tex+A%2E+Sordahl%2E+1986%2E+Evolutionary+aspects+of+avian+distraction+display%3A+variation+in+american+avocet+and+black%2Dnecked+stilt+antipredator+behavior%2E+In+Robert+W%2E+Mitchell+and+Nicholas+S%2E+Thompson%2C+edit)
74. Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Na Zou, Hanjie Chen, and Xia Hu. 2025. [Stop overthinking: A survey on efficient reasoning for large language models](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/). Transactions on Machine Learning Research.
75. Christopher Summerfield, Lennart Luettgau, Magda Dubois, Hannah Rose Kirk, Kobi Hackenburg, Catherine Fist, Katarina Slama, Nicola Ding, Rebecca Anselmetti, Andrew Strait, Mario Giulianelli, and Cozmin Ududec. 2025. Lessons from a chimp: AI "scheming" and the quest for ape language. arXiv. [find](https://scholar.google.com/scholar?q=Christopher+Summerfield%2C+Lennart+Luettgau%2C+Magda+Dubois%2C+Hannah+Rose+Kirk%2C+Kobi+Hackenburg%2C+Catherine+Fist%2C+Katarina+Slama%2C+Nicola+Ding%2C+Rebecca+Anselmetti%2C+Andrew+Strait%2C+Mario+Giulianelli%2C+and+Cozmi)
76. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penche… [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Aishwarya+Kamath%2C+Johan+Ferret%2C+Shreya+Pathak%2C+Nino+Vieillard%2C+Ramona+Merhej%2C+Sarah+Perrin%2C+Tatiana+Matejovicova%2C+Alexandre+Ram%C3%A9%2C+Morgane+Rivi%C3%A8re%2C+Louis+Rouillard%2C+Thomas+Mesnard%2C+Geoffrey)
77. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. 2024. [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://md.research.elamur.ai/free/en/papers/trivedi-2024-appworld-controllable-world-apps-people/). Proceedings of the 62nd Annual Meeting of the Associa…
78. Constantin Venhoff, Iván Arcuschin, Philip Torr, Arthur Conmy, and Neel Nanda. 2025. Understanding reasoning in thinking language models via steering vectors. In Workshop on Reasoning and Planning for Large Language Models. [find](https://scholar.google.com/scholar?q=Constantin+Venhoff%2C+Iv%C3%A1n+Arcuschin%2C+Philip+Torr%2C+Arthur+Conmy%2C+and+Neel+Nanda%2E+2025%2E+Understanding+reasoning+in+thinking+language+models+via+steering+vectors%2E+In+Workshop+on+Reasoning+and+Planning+for)
79. J. Von Neumann and O. Morgenstern. 1944. Theory of games and economic behavior. Theory of games and economic behavior. Princeton University Press, Princeton, NJ, US. [find](https://scholar.google.com/scholar?q=J%2E+Von+Neumann+and+O%2E+Morgenstern%2E+1944%2E+Theory+of+games+and+economic+behavior%2E+Theory+of+games+and+economic+behavior%2E+Princeton+University+Press%2C+Princeton%2C+NJ%2C+US%2E)
80. Francis Rhys Ward, Francesca Toni, and Francesco Belardinelli. 2023. Defining Deception in Structural Causal Games. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’23, page 2902–2904, Richland, SC. International Foundation for Autonomous Agents… [find](https://scholar.google.com/scholar?q=Francis+Rhys+Ward%2C+Francesca+Toni%2C+and+Francesco+Belardinelli%2E+2023%2E+Defining+Deception+in+Structural+Causal+Games%2E+In+Proceedings+of+the+2023+International+Conference+on+Autonomous+Agents+and+Multiag)
81. Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Ben Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, et al. 2024. Livebench: A challenging, contamination-free llm benchmark. arXiv preprint arXiv:2406.19314. [find](https://scholar.google.com/scholar?q=Colin+White%2C+Samuel+Dooley%2C+Manley+Roberts%2C+Arka+Pal%2C+Ben+Feuer%2C+Siddhartha+Jain%2C+Ravid+Shwartz%2DZiv%2C+Neel+Jain%2C+Khalid+Saifullah%2C+Siddartha+Naidu%2C+et+al%2E+2024%2E+Livebench%3A+A+challenging%2C+contamination%2D)
82. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoe… — [Transformers: State-of-the-Art Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
83. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jin… — [Qwen3 Technical Report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
84. Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou, Pinelopi Papalampidi, Somit Gupta, Tsendsuren Munkhdalai, Lu Wang, and Shyam Upadhyay. 2025. [Do llms really need 10+ thoughts for "find the time 1000 days later"?](https://md.research.elamur.ai/free/en/papers/zhang-2026-do-llms-really-need-10/) towards structural understanding of llm overthinking.
85. Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Yan Xia, Wenshan Wu, Ting Song, Man Lan, and Furu Wei. 2024. LLM as a mastermind: A survey of strategic reasoning with large language models. In First Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Yadong+Zhang%2C+Shaoguang+Mao%2C+Tao+Ge%2C+Xun+Wang%2C+Yan+Xia%2C+Wenshan+Wu%2C+Ting+Song%2C+Man+Lan%2C+and+Furu+Wei%2E+2024%2E+LLM+as+a+mastermind%3A+A+survey+of+strategic+reasoning+with+large+language+models%2E+In+First+Co)
86. 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.

*Updated 31 October 2026*