---
title: "Latent Inter-User Difference Modeling for LLM Personalization"
description: "The authors propose DEP (Difference-aware Embedding-based Personalization), which moves the… 88 references, cited by 8 papers here."
authors: ["Yilun Qiu", "Tianhao Shi", "Xiaoyan Zhao", "Fengbin Zhu", "Yang Zhang", "Fuli Feng"]
year: 2025
venue: "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2025.emnlp-main.536/"
doi: "10.18653/v1/2025.emnlp-main.536"
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/qiu-2025-latent-inter-user-difference-modeling/"
---
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# Latent Inter-User Difference Modeling for LLM Personalization

**Authors:** Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, [Fengbin Zhu](https://md.research.elamur.ai/free/en/authors/fengbin-zhu/), [Yang Zhang](https://md.research.elamur.ai/free/en/authors/yang-zhang/) and [Fuli Feng](https://md.research.elamur.ai/free/en/authors/fuli-feng/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)) · **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/2025.emnlp-main.536/) · [PDF](https://aclanthology.org/2025.emnlp-main.536.pdf) · [DOI 10.18653/v1/2025.emnlp-main.536](https://doi.org/10.18653/v1/2025.emnlp-main.536) · [OpenAlex](https://openalex.org/W4416036679)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/qiu-2025-latent-inter-user-difference-modeling/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most LLM personalization conditions generation on a user's own history. However, what makes someone distinctive is how they deviate from other people. An earlier method, DPL, asked the LLM to compare users in natural language. That comparison is hard to steer and fills the prompt with other users' raw text.

The authors propose DEP (Difference-aware Embedding-based Personalization), which moves the comparison into embedding space:
- For each retrieved past review by the target user, a frozen text encoder produces a user-specific vector.
- Reviews of the same item by other people are encoded the same way. Averaging the differences between the user's vector and theirs yields a difference-aware vector.
- Both vectors pass through a sparse autoencoder that keeps only salient features.
- The compressed outputs enter a frozen LLM as soft prompts.

On personalized review generation across several domains, DEP outperforms baseline methods, including DPL, on multiple metrics. Gains are largest for users with unusual behavior. The work suggests that latent-space contrast is a compact, more controllable alternative to prompt-based user comparison.

## Key points
- **Difference-aware embedding.** For item i, it is the mean of (e_his − e_uj) over all m peers who reviewed i. It is paired with the user-specific embedding, which supplies the behavioral context.
- **SAE filtering.** The SAE (Huben et al., 2024) uses an ℓ1 sparsity constraint to filter redundant or noisy features. It is fine-tuned to align the representations with the frozen LLM's internal understanding.
- **Setting and data.** DEP follows the memory-retrieval paradigm, so no LLM retraining is needed. It is evaluated on review generation using public data from Ni et al. (2019), released under the MIT license.
- **Limitations.** The authors note dependence on sufficient user history (cold-start is weak), extra compute for embeddings and the SAE, and evaluation limited to review generation.
- **Code.** Available at github.com/SnowCharmQ/DEP (EMNLP 2025, pp. 10599–10617).

*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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- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 108 works · 112,698 citations
- [Chinese University of Hong Kong](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) — University, Hong Kong, China · 126 works · 14,073 citations

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## Cite

Cite as: Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang and Fuli Feng. 2025. Latent Inter-User Difference Modeling for LLM Personalization. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{qiu2025latent,
  title = {{Latent Inter-User Difference Modeling for LLM Personalization}},
  author = {Yilun Qiu, Tianhao Shi, Xiaoyan Zhao, Fengbin Zhu, Yang Zhang and Fuli Feng},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.536},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.536}
}
```

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- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/7/)

## References

88 references, 26 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Steven Au, Cameron J Dimacali, Ojasmitha Pedirappa-gari, Namyong Park, Franck Dernoncourt, Yu Wang, Nikos Kanakaris, Hanieh Deilamsalehy, Ryan A Rossi, and Nesreen K Ahmed. 2025. Personalized graph-based retrieval for large language models. arXiv preprint arXiv:2501.02157. [find](https://scholar.google.com/scholar?q=Steven+Au%2C+Cameron+J+Dimacali%2C+Ojasmitha+Pedirappa%2Dgari%2C+Namyong+Park%2C+Franck+Dernoncourt%2C+Yu+Wang%2C+Nikos+Kanakaris%2C+Hanieh+Deilamsalehy%2C+Ryan+A+Rossi%2C+and+Nesreen+K+Ahmed%2E+2025%2E+Personalized+graph%2Dba)
3. Satanjeev Banerjee and Alon Lavie. 2005. [Meteor: An automatic metric for mt evaluation with improved correlation with human judgments](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/). In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65–72.
4. Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. [Longformer: The long-document transformer](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/). arXiv preprint arXiv:2004.05150.
5. Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024a. [Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/). arXiv preprint arXiv:2402.03216.
6. Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, and 1 others. 2024b. When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web, 27(4):42. [find](https://scholar.google.com/scholar?q=Jin+Chen%2C+Zheng+Liu%2C+Xu+Huang%2C+Chenwang+Wu%2C+Qi+Liu%2C+Gangwei+Jiang%2C+Yuanhao+Pu%2C+Yuxuan+Lei%2C+Xiaolong+Chen%2C+Xingmei+Wang%2C+and+1+others%2E+2024b%2E+When+large+language+models+meet+personalization%3A+Perspectiv)
7. Ruizhe Chen, Xiaotian Zhang, Meng Luo, Wenhao Chai, and Zuozhu Liu. 2025a. PAD: personalized alignment of llms at decoding-time. In The Thirteenth International Conference on Learning Representations, ICLR 2025, 2025. [find](https://scholar.google.com/scholar?q=Ruizhe+Chen%2C+Xiaotian+Zhang%2C+Meng+Luo%2C+Wenhao+Chai%2C+and+Zuozhu+Liu%2E+2025a%2E+PAD%3A+personalized+alignment+of+llms+at+decoding%2Dtime%2E+In+The+Thirteenth+International+Conference+on+Learning+Representations%2C)
8. Yuxin Chen, Yiran Zhao, Yang Zhang, An Zhang, Kenji Kawaguchi, Shafiq Joty, Junnan Li, Tat-Seng Chua, Michael Qizhe Shieh, and Wenxuan Zhang. 2025b. The emergence of abstract thought in large language models beyond any language. arXiv preprint arXiv:2506.09890. — [arXiv:2506.09890](https://arxiv.org/abs/2506.09890) [find](https://scholar.google.com/scholar?q=Yuxin+Chen%2C+Yiran+Zhao%2C+Yang+Zhang%2C+An+Zhang%2C+Kenji+Kawaguchi%2C+Shafiq+Joty%2C+Junnan+Li%2C+Tat%2DSeng+Chua%2C+Michael+Qizhe+Shieh%2C+and+Wenxuan+Zhang%2E+2025b%2E+The+emergence+of+abstract+thought+in+large+language)
9. Tri Dao. 2023. [Flashattention-2: Faster attention with better parallelism and work partitioning](https://md.research.elamur.ai/free/en/papers/dao-2023-flashattention-2-faster-attention-better/). arXiv preprint arXiv:2307.08691.
10. Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, and Dima Kuzmin. 2024. User embedding model for personalized language prompting. In Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024), pages 124–131. [find](https://scholar.google.com/scholar?q=Sumanth+Doddapaneni%2C+Krishna+Sayana%2C+Ambarish+Jash%2C+Sukhdeep+Sodhi%2C+and+Dima+Kuzmin%2E+2024%2E+User+embedding+model+for+personalized+language+prompting%2E+In+Proceedings+of+the+1st+Workshop+on+Personalizati)
11. Junjia Du, Yadi Liu, Hongcheng Guo, Jiawei Wang, Haojian Huang, Yunyi Ni, and Zhoujun Li. 2025. DependEval: Benchmarking LLMs for repository dependency understanding. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7150–7179. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Junjia+Du%2C+Yadi+Liu%2C+Hongcheng+Guo%2C+Jiawei+Wang%2C+Haojian+Huang%2C+Yunyi+Ni%2C+and+Zhoujun+Li%2E+2025%2E+DependEval%3A+Benchmarking+LLMs+for+repository+dependency+understanding%2E+In+Findings+of+the+Association+fo)
12. Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, and Tat-Seng Chua. 2025a. Alphaedit: Null-space constrained knowledge editing for language models. In The Thirteenth International Conference on Learning Representations, ICLR 2025, 2025. [find](https://scholar.google.com/scholar?q=Junfeng+Fang%2C+Houcheng+Jiang%2C+Kun+Wang%2C+Yunshan+Ma%2C+Jie+Shi%2C+Xiang+Wang%2C+Xiangnan+He%2C+and+Tat%2DSeng+Chua%2E+2025a%2E+Alphaedit%3A+Null%2Dspace+constrained+knowledge+editing+for+language+models%2E+In+The+Thirteen)
13. Yixiong Fang, Tianran Sun, Yuling Shi, and Xiaodong Gu. 2025b. Attentionrag: Attention-guided context pruning in retrieval-augmented generation. arXiv preprint arXiv:2503.10720. — [arXiv:2503.10720](https://arxiv.org/abs/2503.10720) [find](https://scholar.google.com/scholar?q=Yixiong+Fang%2C+Tianran+Sun%2C+Yuling+Shi%2C+and+Xiaodong+Gu%2E+2025b%2E+Attentionrag%3A+Attention%2Dguided+context+pruning+in+retrieval%2Daugmented+generation%2E+arXiv+preprint+arXiv%3A2503%2E10720%2E+%E2%80%94+%5BarXiv%3A2503%2E10720%5D%28h)
14. Yixiong Fang, Tianran Sun, Yuling Shi, Min Wang, and Xiaodong Gu. 2025c. Lastingbench: Defend benchmarks against knowledge leakage. arXiv preprint arXiv:2506.21614. [find](https://scholar.google.com/scholar?q=Yixiong+Fang%2C+Tianran+Sun%2C+Yuling+Shi%2C+Min+Wang%2C+and+Xiaodong+Gu%2E+2025c%2E+Lastingbench%3A+Defend+benchmarks+against+knowledge+leakage%2E+arXiv+preprint+arXiv%3A2506%2E21614%2E)
15. Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249–256. [find](https://scholar.google.com/scholar?q=Xavier+Glorot+and+Yoshua+Bengio%2E+2010%2E+Understanding+the+difficulty+of+training+deep+feedforward+neural+networks%2E+In+Proceedings+of+the+thirteenth+international+conference+on+artificial+intelligence+a)
16. 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.
17. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
18. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web, pages 173–182. [find](https://scholar.google.com/scholar?q=Xiangnan+He%2C+Lizi+Liao%2C+Hanwang+Zhang%2C+Liqiang+Nie%2C+Xia+Hu%2C+and+Tat%2DSeng+Chua%2E+2017%2E+Neural+collaborative+filtering%2E+In+Proceedings+of+the+26th+international+conference+on+world+wide+web%2C+pages+173%E2%80%9318)
19. Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, and Tat-Seng Chua. 2025. Llm2rec: Large language models are powerful embedding models for sequential recommendation. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pages 896–907. [find](https://scholar.google.com/scholar?q=Yingzhi+He%2C+Xiaohao+Liu%2C+An+Zhang%2C+Yunshan+Ma%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Llm2rec%3A+Large+language+models+are+powerful+embedding+models+for+sequential+recommendation%2E+In+Proceedings+of+the+31st+ACM+SIGKD)
20. Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. 2024. Bridging language and items for retrieval and recommendation. arXiv preprint arXiv:2403.03952. — [arXiv:2403.03952](https://arxiv.org/abs/2403.03952) [find](https://scholar.google.com/scholar?q=Yupeng+Hou%2C+Jiacheng+Li%2C+Zhankui+He%2C+An+Yan%2C+Xiusi+Chen%2C+and+Julian+McAuley%2E+2024%2E+Bridging+language+and+items+for+retrieval+and+recommendation%2E+arXiv+preprint+arXiv%3A2403%2E03952%2E+%E2%80%94+%5BarXiv%3A2403%2E03952%5D%28h)
21. Robert Huben, Hoagy Cunningham, Logan Riggs, Aidan Ewart, and Lee Sharkey. 2024. Sparse autoencoders find highly interpretable features in language models. In The Twelfth International Conference on Learning Representations, ICLR 2024, 2024. [find](https://scholar.google.com/scholar?q=Robert+Huben%2C+Hoagy+Cunningham%2C+Logan+Riggs%2C+Aidan+Ewart%2C+and+Lee+Sharkey%2E+2024%2E+Sparse+autoencoders+find+highly+interpretable+features+in+language+models%2E+In+The+Twelfth+International+Conference+on+L)
22. Caglar Irmak, Beth Vallen, and Sankar Sen. 2010. You like what i like, but i don’t like what you like: Uniqueness motivations in product preferences. Journal of Consumer Research, 37(3):443–455. [find](https://scholar.google.com/scholar?q=Caglar+Irmak%2C+Beth+Vallen%2C+and+Sankar+Sen%2E+2010%2E+You+like+what+i+like%2C+but+i+don%E2%80%99t+like+what+you+like%3A+Uniqueness+motivations+in+product+preferences%2E+Journal+of+Consumer+Research%2C+37%283%29%3A443%E2%80%93455%2E)
23. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022. [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/).
24. Trans. Mach. Learn. Res., 2022. [find](https://scholar.google.com/scholar?q=Trans%2E+Mach%2E+Learn%2E+Res%2E%2C+2022%2E)
25. Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew M. Bean, Katerina Margatina, Rafael Mosquera Gómez, Juan Ciro, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott Hale. 2024. The PRISM alignment dataset: What participatory, representative and individualised human feedback revea… [find](https://scholar.google.com/scholar?q=Hannah+Rose+Kirk%2C+Alexander+Whitefield%2C+Paul+R%C3%B6ttger%2C+Andrew+M%2E+Bean%2C+Katerina+Margatina%2C+Rafael+Mosquera+G%C3%B3mez%2C+Juan+Ciro%2C+Max+Bartolo%2C+Adina+Williams%2C+He+He%2C+Bertie+Vidgen%2C+and+Scott+Hale%2E+2024%2E+The)
26. Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra, Alireza Salemi, Ryan A Rossi, Franck Dernoncourt, Hanieh Deilamsalehy, Xiang Chen, Ruiyi Zhang, Shubham Agarwal, and 1 others. 2024. Longlamp: A benchmark for personalized long-form text generation. arXiv preprint arXiv:2407.11016. — [arXiv:2407.11016](https://arxiv.org/abs/2407.11016) [find](https://scholar.google.com/scholar?q=Ishita+Kumar%2C+Snigdha+Viswanathan%2C+Sushrita+Yerra%2C+Alireza+Salemi%2C+Ryan+A+Rossi%2C+Franck+Dernoncourt%2C+Hanieh+Deilamsalehy%2C+Xiang+Chen%2C+Ruiyi+Zhang%2C+Shubham+Agarwal%2C+and+1+others%2E+2024%2E+Longlamp%3A+A+benc)
27. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph 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 29th Symposium on Operating Systems Principles, pages 611–626.
28. Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, and Michael Bendersky. 2023. Teach llms to personalize– an approach inspired by writing education. arXiv preprint arXiv:2308.07968. — [arXiv:2308.07968](https://arxiv.org/abs/2308.07968) [find](https://scholar.google.com/scholar?q=Cheng+Li%2C+Mingyang+Zhang%2C+Qiaozhu+Mei%2C+Yaqing+Wang%2C+Spurthi+Amba+Hombaiah%2C+Yi+Liang%2C+and+Michael+Bendersky%2E+2023%2E+Teach+llms+to+personalize%E2%80%93+an+approach+inspired+by+writing+education%2E+arXiv+preprint+a)
29. CHEN Liang, Li Shen, Yang Deng, Xiaoyan Zhao, Bin Liang, and Kam-Fai Wong. Pearl: Towards permutation-resilient llms. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=CHEN+Liang%2C+Li+Shen%2C+Yang+Deng%2C+Xiaoyan+Zhao%2C+Bin+Liang%2C+and+Kam%2DFai+Wong%2E+Pearl%3A+Towards+permutation%2Dresilient+llms%2E+In+The+Thirteenth+International+Conference+on+Learning+Representations%2E)
30. Chin-Yew Lin. 2004. [Rouge: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text summarization branches out, pages 74–81. the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), pages 198–219.
31. Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. [Justifying recommendations using distantly-labeled reviews and fine-grained aspects](https://md.research.elamur.ai/free/en/papers/ni-2019-justifying-recommendations-distantly-labeled-reviews/). In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (…
32. Zijie Lin, Yang Zhang, Xiaoyan Zhao, Fengbin Zhu, Fuli Feng, and Tat-Seng Chua. 2025. Igd: Token decisiveness modeling via information gain in llms for personalized recommendation. arXiv preprint arXiv:2506.13229. — [arXiv:2506.13229](https://arxiv.org/abs/2506.13229) [find](https://scholar.google.com/scholar?q=Zijie+Lin%2C+Yang+Zhang%2C+Xiaoyan+Zhao%2C+Fengbin+Zhu%2C+Fuli+Feng%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Igd%3A+Token+decisiveness+modeling+via+information+gain+in+llms+for+personalized+recommendation%2E+arXiv+preprint+arXi)
33. Lin Ning, Luyang Liu, Jiaxing Wu, Neo Wu, Devora Berlowitz, Sushant Prakash, Bradley Green, Shawn O’Banion, and Jun Xie. 2025. User-llm: Efficient LLM contextualization with user embeddings. In Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, pages 1219–1223… [find](https://scholar.google.com/scholar?q=Lin+Ning%2C+Luyang+Liu%2C+Jiaxing+Wu%2C+Neo+Wu%2C+Devora+Berlowitz%2C+Sushant+Prakash%2C+Bradley+Green%2C+Shawn+O%E2%80%99Banion%2C+and+Jun+Xie%2E+2025%2E+User%2Dllm%3A+Efficient+LLM+contextualization+with+user+embeddings%2E+In+Compan)
34. Chang Liu, Yimeng Bai, Xiaoyan Zhao, Yang Zhang, Fuli Feng, and Wenge Rong. 2025a. Dis-crec: Disentangled semantic-collaborative modeling for generative recommendation. arXiv preprint arXiv:2506.15576. — [arXiv:2506.15576](https://arxiv.org/abs/2506.15576) [find](https://scholar.google.com/scholar?q=Chang+Liu%2C+Yimeng+Bai%2C+Xiaoyan+Zhao%2C+Yang+Zhang%2C+Fuli+Feng%2C+and+Wenge+Rong%2E+2025a%2E+Dis%2Dcrec%3A+Disentangled+semantic%2Dcollaborative+modeling+for+generative+recommendation%2E+arXiv+preprint+arXiv%3A2506%2E15576)
35. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.
36. Jiahong Liu, Zexuan Qiu, Zhongyang Li, Quanyu Dai, Jieming Zhu, Minda Hu, Menglin Yang, and Irwin King. 2025b. A survey of personalized large language models: Progress and future directions. arXiv preprint arXiv:2502.11528. — [arXiv:2502.11528](https://arxiv.org/abs/2502.11528) [find](https://scholar.google.com/scholar?q=Jiahong+Liu%2C+Zexuan+Qiu%2C+Zhongyang+Li%2C+Quanyu+Dai%2C+Jieming+Zhu%2C+Minda+Hu%2C+Menglin+Yang%2C+and+Irwin+King%2E+2025b%2E+A+survey+of+personalized+large+language+models%3A+Progress+and+future+directions%2E+arXiv+pre)
37. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, and 1 others. 2019. [Pytorch: An imperative style, high-performance deep learning library](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/). Advances in neural information processing systems, 32.
38. Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, and Zhicheng Dou. 2025c. [LLMs + persona-plug = personalized LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-llms-persona-plug-personalized-llms/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9373– 9385. As…
39. Xiaohao Liu, Xiaobo Xia, Weixiang Zhao, Manyi Zhang, Xianzhi Yu, Xiu Su, Shuo Yang, See-Kiong Ng, and Tat-Seng Chua. 2025d. L-mtp: Leap multi-token prediction beyond adjacent context for large language models. arXiv preprint arXiv:2505.17505. — [arXiv:2505.17505](https://arxiv.org/abs/2505.17505) [find](https://scholar.google.com/scholar?q=Xiaohao+Liu%2C+Xiaobo+Xia%2C+Weixiang+Zhao%2C+Manyi+Zhang%2C+Xianzhi+Yu%2C+Xiu+Su%2C+Shuo+Yang%2C+See%2DKiong+Ng%2C+and+Tat%2DSeng+Chua%2E+2025d%2E+L%2Dmtp%3A+Leap+multi%2Dtoken+prediction+beyond+adjacent+context+for+large+languag)
40. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, 2019. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2019%2E+Decoupled+weight+decay+regularization%2E+In+7th+International+Conference+on+Learning+Representations%2C+ICLR+2019%2C+2019%2E)
41. Jisoo Mok, Ik-hwan Kim, Sangkwon Park, and Sungroh Yoon. 2025. Exploring the potential of LLMs as personalized assistants: Dataset, evaluation, and analysis. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10212–10239. Associa… [find](https://scholar.google.com/scholar?q=Jisoo+Mok%2C+Ik%2Dhwan+Kim%2C+Sangkwon+Park%2C+and+Sungroh+Yoon%2E+2025%2E+Exploring+the+potential+of+LLMs+as+personalized+assistants%3A+Dataset%2C+evaluation%2C+and+analysis%2E+In+Proceedings+of+the+63rd+Annual+Meeting+)
42. Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Bahareh Sarrafzadeh, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, and Tara Safavi. 2024. Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers. In Proceedings of Qiyao Pe… [find](https://scholar.google.com/scholar?q=Sheshera+Mysore%2C+Zhuoran+Lu%2C+Mengting+Wan%2C+Longqi+Yang%2C+Bahareh+Sarrafzadeh%2C+Steve+Menezes%2C+Tina+Baghaee%2C+Emmanuel+Barajas+Gonzalez%2C+Jennifer+Neville%2C+and+Tara+Safavi%2E+2024%2E+Pearl%3A+Personalizing+large)
43. Matt Post. 2018. [A call for clarity in reporting BLEU scores](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/). In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186– 191.
44. Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, and Tat-Seng Chua. 2025. Measuring what makes you unique: Difference-aware user modeling for enhancing LLM personalization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21258– 21277.… [find](https://scholar.google.com/scholar?q=Yilun+Qiu%2C+Xiaoyan+Zhao%2C+Yang+Zhang%2C+Yimeng+Bai%2C+Wenjie+Wang%2C+Hong+Cheng%2C+Fuli+Feng%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Measuring+what+makes+you+unique%3A+Difference%2Daware+user+modeling+for+enhancing+LLM+personal)
45. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1– 16. IEEE. [find](https://scholar.google.com/scholar?q=Samyam+Rajbhandari%2C+Jeff+Rasley%2C+Olatunji+Ruwase%2C+and+Yuxiong+He%2E+2020%2E+Zero%3A+Memory+optimizations+toward+training+trillion+parameter+models%2E+In+SC20%3A+International+Conference+for+High+Performance+Com)
46. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 3505–3506. [find](https://scholar.google.com/scholar?q=Jeff+Rasley%2C+Samyam+Rajbhandari%2C+Olatunji+Ruwase%2C+and+Yuxiong+He%2E+2020%2E+Deepspeed%3A+System+optimizations+enable+training+deep+learning+models+with+over+100+billion+parameters%2E+In+Proceedings+of+the+26t)
47. Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023a. Integrating summarization and retrieval for enhanced personalization via large language models. arXiv preprint arXiv:2310.20081. — [arXiv:2310.20081](https://arxiv.org/abs/2310.20081) [find](https://scholar.google.com/scholar?q=Chris+Richardson%2C+Yao+Zhang%2C+Kellen+Gillespie%2C+Sudipta+Kar%2C+Arshdeep+Singh%2C+Zeynab+Raeesy%2C+Omar+Zia+Khan%2C+and+Abhinav+Sethy%2E+2023a%2E+Integrating+summarization+and+retrieval+for+enhanced+personalization)
48. Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023b. Integrating summarization and retrieval for enhanced personalization via large language models. arXiv preprint arXiv:2310.20081. — [arXiv:2310.20081](https://arxiv.org/abs/2310.20081) [find](https://scholar.google.com/scholar?q=Chris+Richardson%2C+Yao+Zhang%2C+Kellen+Gillespie%2C+Sudipta+Kar%2C+Arshdeep+Singh%2C+Zeynab+Raeesy%2C+Omar+Zia+Khan%2C+and+Abhinav+Sethy%2E+2023b%2E+Integrating+summarization+and+retrieval+for+enhanced+personalization)
49. Stephen Robertson, Hugo Zaragoza, and 1 others. 2009. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333–389. [find](https://scholar.google.com/scholar?q=Stephen+Robertson%2C+Hugo+Zaragoza%2C+and+1+others%2E+2009%2E+The+probabilistic+relevance+framework%3A+Bm25+and+beyond%2E+Foundations+and+Trends%C2%AE+in+Information+Retrieval%2C+3%284%29%3A333%E2%80%93389%2E)
50. Alireza Salemi, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, Tao Chen, Zhuowan Li, Michael Bendersky, and Hamed Zamani. 2025. Reasoning-enhanced self-training for long-form personalized text generation. arXiv preprint arXiv:2501.04167. — [arXiv:2501.04167](https://arxiv.org/abs/2501.04167) [find](https://scholar.google.com/scholar?q=Alireza+Salemi%2C+Cheng+Li%2C+Mingyang+Zhang%2C+Qiaozhu+Mei%2C+Weize+Kong%2C+Tao+Chen%2C+Zhuowan+Li%2C+Michael+Bendersky%2C+and+Hamed+Zamani%2E+2025%2E+Reasoning%2Denhanced+self%2Dtraining+for+long%2Dform+personalized+text+gen)
51. Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2024. [LaMP: When large language models meet personalization](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392.
52. Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie. 2024a. Mome: Mixture of multimodal experts for generalist multimodal large language models. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancou… [find](https://scholar.google.com/scholar?q=Leyang+Shen%2C+Gongwei+Chen%2C+Rui+Shao%2C+Weili+Guan%2C+and+Liqiang+Nie%2E+2024a%2E+Mome%3A+Mixture+of+multimodal+experts+for+generalist+multimodal+large+language+models%2E+In+Advances+in+Neural+Information+Processi)
53. Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, and Xi Xiao. 2024b. Pmg: Personalized multimodal generation with large language models. In Proceedings of the ACM Web Conference 2024, pages 3833–3843. [find](https://scholar.google.com/scholar?q=Xiaoteng+Shen%2C+Rui+Zhang%2C+Xiaoyan+Zhao%2C+Jieming+Zhu%2C+and+Xi+Xiao%2E+2024b%2E+Pmg%3A+Personalized+multimodal+generation+with+large+language+models%2E+In+Proceedings+of+the+ACM+Web+Conference+2024%2C+pages+3833%E2%80%933)
54. Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, and Yueting Zhuang. 2024c. Taskbench: Benchmarking large language models for task automation. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Sy… [find](https://scholar.google.com/scholar?q=Yongliang+Shen%2C+Kaitao+Song%2C+Xu+Tan%2C+Wenqi+Zhang%2C+Kan+Ren%2C+Siyu+Yuan%2C+Weiming+Lu%2C+Dongsheng+Li%2C+and+Yueting+Zhuang%2E+2024c%2E+Taskbench%3A+Benchmarking+large+language+models+for+task+automation%2E+In+Advance)
55. Leheng Sheng, Changshuo Shen, Weixiang Zhao, Junfeng Fang, Xiaohao Liu, Zhenkai Liang, Xiang Wang, An Zhang, and Tat-Seng Chua. 2025. Alphasteer: Learning refusal steering with principled null-space constraint. arXiv preprint arXiv:2506.07022. — [arXiv:2506.07022](https://arxiv.org/abs/2506.07022) [find](https://scholar.google.com/scholar?q=Leheng+Sheng%2C+Changshuo+Shen%2C+Weixiang+Zhao%2C+Junfeng+Fang%2C+Xiaohao+Liu%2C+Zhenkai+Liang%2C+Xiang+Wang%2C+An+Zhang%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Alphasteer%3A+Learning+refusal+steering+with+principled+null%2Dspace+c)
56. Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, and Han Li. 2025. Retrieval augmented generation with collaborative filtering for personalized text generation. arXiv preprint arXiv:2504.05731. — [arXiv:2504.05731](https://arxiv.org/abs/2504.05731) [find](https://scholar.google.com/scholar?q=Teng+Shi%2C+Jun+Xu%2C+Xiao+Zhang%2C+Xiaoxue+Zang%2C+Kai+Zheng%2C+Yang+Song%2C+and+Han+Li%2E+2025%2E+Retrieval+augmented+generation+with+collaborative+filtering+for+personalized+text+generation%2E+arXiv+preprint+arXiv%3A2)
57. Charles R Snyder and Howard L Fromkin. 1977. Abnormality as a positive characteristic: The development and validation of a scale measuring need for uniqueness. Journal of Abnormal Psychology, 86(5):518. [find](https://scholar.google.com/scholar?q=Charles+R+Snyder+and+Howard+L+Fromkin%2E+1977%2E+Abnormality+as+a+positive+characteristic%3A+The+development+and+validation+of+a+scale+measuring+need+for+uniqueness%2E+Journal+of+Abnormal+Psychology%2C+86%285%29%3A51)
58. Charles R Snyder and Howard L Fromkin. 2012. Uniqueness: The human pursuit of difference. Springer Science & Business Media. [find](https://scholar.google.com/scholar?q=Charles+R+Snyder+and+Howard+L+Fromkin%2E+2012%2E+Uniqueness%3A+The+human+pursuit+of+difference%2E+Springer+Science+%26+Business+Media%2E)
59. Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, and Heng Ji. 2025. [Persona-DB: Efficient large language model personalization for response prediction with collaborative data refinement](https://md.research.elamur.ai/free/en/papers/sun-2025-persona-db-efficient-large-language/). In Proceedings of the 31st International Conference on Computatio…
60. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, and 1 others. 2025. Gemma 3 technical report. arXiv preprint arXiv:2503.19786. [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+and+1+others%2E+2025%2E+Gemma+3+technical+rep)
61. Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pages 165–174. [find](https://scholar.google.com/scholar?q=Xiang+Wang%2C+Xiangnan+He%2C+Meng+Wang%2C+Fuli+Feng%2C+and+Tat%2DSeng+Chua%2E+2019%2E+Neural+graph+collaborative+filtering%2E+In+Proceedings+of+the+42nd+international+ACM+SIGIR+conference+on+Research+and+development+)
62. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and 1 others. 2020. [Transformers: State-of-the-art natural language processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/). In Proceedings of the 2020 conference on empirical methods in natural lan…
63. Yiyan Xu, Wenjie Wang, Yang Zhang, Biao Tang, Peng Yan, Fuli Feng, and Xiangnan He. 2025a. Personalized image generation with large multimodal models. In Proceedings of the ACM on Web Conference 2025, pages 264–274. [find](https://scholar.google.com/scholar?q=Yiyan+Xu%2C+Wenjie+Wang%2C+Yang+Zhang%2C+Biao+Tang%2C+Peng+Yan%2C+Fuli+Feng%2C+and+Xiangnan+He%2E+2025a%2E+Personalized+image+generation+with+large+multimodal+models%2E+In+Proceedings+of+the+ACM+on+Web+Conference+2025%2C)
64. Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, and Tat-Seng Chua. 2025b. [Personalized generation in large model era: A survey](https://md.research.elamur.ai/free/en/papers/xu-2025-personalized-generation-large-model-era/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),…
65. Yiyan Xu, Wuqiang Zheng, Wenjie Wang, Fengbin Zhu, Xinting Hu, Yang Zhang, Fuli Feng, and Tat-Seng Chua. 2025c. Drc: Enhancing personalized image generation via disentangled representation composition. arXiv preprint arXiv:2504.17349. — [arXiv:2504.17349](https://arxiv.org/abs/2504.17349) [find](https://scholar.google.com/scholar?q=Yiyan+Xu%2C+Wuqiang+Zheng%2C+Wenjie+Wang%2C+Fengbin+Zhu%2C+Xinting+Hu%2C+Yang+Zhang%2C+Fuli+Feng%2C+and+Tat%2DSeng+Chua%2E+2025c%2E+Drc%3A+Enhancing+personalized+image+generation+via+disentangled+representation+composition)
66. 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.
67. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, and 1 others. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115. [find](https://scholar.google.com/scholar?q=An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+and+1+others%2E+2024%2E+Qwen2%2E+5+technical+report%2E+arXiv+preprint+arXiv%3A2412%2E15115)
68. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023. [React: Synergizing reasoning and acting in language models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). In The Eleventh International Conference on Learning Representations, ICLR 2023, 2023.
69. Zijun Yao, Yantao Liu, Yanxu Chen, Jianhui Chen, Junfeng Fang, Lei Hou, Juanzi Li, and Tat-Seng Chua. 2025. Are reasoning models more prone to hallucination? arXiv preprint arXiv:2505.23646. — [arXiv:2505.23646](https://arxiv.org/abs/2505.23646) [find](https://scholar.google.com/scholar?q=Zijun+Yao%2C+Yantao+Liu%2C+Yanxu+Chen%2C+Jianhui+Chen%2C+Junfeng+Fang%2C+Lei+Hou%2C+Juanzi+Li%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Are+reasoning+models+more+prone+to+hallucination%3F+arXiv+preprint+arXiv%3A2505%2E23646%2E+%E2%80%94+%5BarXiv%3A)
70. Mert Yazan, Suzan Verberne, and Frederik Situmeang. 2025. Improving rag for personalization with author features and contrastive examples. In European Conference on Information Retrieval, pages 408–416. Springer. [find](https://scholar.google.com/scholar?q=Mert+Yazan%2C+Suzan+Verberne%2C+and+Frederik+Situmeang%2E+2025%2E+Improving+rag+for+personalization+with+author+features+and+contrastive+examples%2E+In+European+Conference+on+Information+Retrieval%2C+pages+408%E2%80%9341)
71. Yoel Zeldes, Amir Zait, Ilia Labzovsky, Danny Karmon, and Efrat Farkash. 2025. Commer: a framework for compressing and merging user data for personalization. arXiv preprint arXiv:2501.03276. — [arXiv:2501.03276](https://arxiv.org/abs/2501.03276) [find](https://scholar.google.com/scholar?q=Yoel+Zeldes%2C+Amir+Zait%2C+Ilia+Labzovsky%2C+Danny+Karmon%2C+and+Efrat+Farkash%2E+2025%2E+Commer%3A+a+framework+for+compressing+and+merging+user+data+for+personalization%2E+arXiv+preprint+arXiv%3A2501%2E03276%2E+%E2%80%94+%5BarXiv%3A)
72. Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, and 1 others. 2025. [Glm-4.5: Agentic, reasoning, and coding (arc) foundation models](https://md.research.elamur.ai/free/en/papers/team-2025-4-glm-4-5-agentic/). arXiv preprint arXiv:2508.06471.
73. Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, and Tat-Seng Chua. 2025a. [Personalized text generation with contrastive activation steering](https://md.research.elamur.ai/free/en/papers/zhang-2025-personalized-text-generation-contrastive-activation/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7128–7141. Ass…
74. Rui Zhang, Yixin Su, Bayu Distiawan Trisedya, Xiaoyan Zhao, Min Yang, Hong Cheng, and Jianzhong Qi. 2023. Autoalign: Fully automatic and effective knowledge graph alignment enabled by large language models. IEEE Transactions on Knowledge and Data Engineering, 36(6):2357–2371. [find](https://scholar.google.com/scholar?q=Rui+Zhang%2C+Yixin+Su%2C+Bayu+Distiawan+Trisedya%2C+Xiaoyan+Zhao%2C+Min+Yang%2C+Hong+Cheng%2C+and+Jianzhong+Qi%2E+2023%2E+Autoalign%3A+Fully+automatic+and+effective+knowledge+graph+alignment+enabled+by+large+language+m)
75. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, 2020. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q%2E+Weinberger%2C+and+Yoav+Artzi%2E+2020%2E+Bertscore%3A+Evaluating+text+generation+with+BERT%2E+In+8th+International+Conference+on+Learning+Representations%2C+ICLR+2)
76. Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, and Xiangnan He. 2024a. Text-like encoding of collaborative information in large language models for recommendation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Associatio… [find](https://scholar.google.com/scholar?q=Yang+Zhang%2C+Keqin+Bao%2C+Ming+Yan%2C+Wenjie+Wang%2C+Fuli+Feng%2C+and+Xiangnan+He%2E+2024a%2E+Text%2Dlike+encoding+of+collaborative+information+in+large+language+models+for+recommendation%2E+In+Proceedings+of+the+62nd)
77. Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2025b. Collm: Integrating collaborative embeddings into large language models for recommendation. IEEE Transactions on Knowledge and Data Engineering. [find](https://scholar.google.com/scholar?q=Yang+Zhang%2C+Fuli+Feng%2C+Jizhi+Zhang%2C+Keqin+Bao%2C+Qifan+Wang%2C+and+Xiangnan+He%2E+2025b%2E+Collm%3A+Integrating+collaborative+embeddings+into+large+language+models+for+recommendation%2E+IEEE+Transactions+on+Knowl)
78. Yang Zhang, Wenxin Xu, Xiaoyan Zhao, Wenjie Wang, Fuli Feng, Xiangnan He, and Tat-Seng Chua. 2025c. Reinforced latent reasoning for llm-based recommendation. arXiv preprint arXiv:2505.19092. — [arXiv:2505.19092](https://arxiv.org/abs/2505.19092) [find](https://scholar.google.com/scholar?q=Yang+Zhang%2C+Wenxin+Xu%2C+Xiaoyan+Zhao%2C+Wenjie+Wang%2C+Fuli+Feng%2C+Xiangnan+He%2C+and+Tat%2DSeng+Chua%2E+2025c%2E+Reinforced+latent+reasoning+for+llm%2Dbased+recommendation%2E+arXiv+preprint+arXiv%3A2505%2E19092%2E+%E2%80%94+%5BarXiv%3A)
79. Zhehao Zhang, Ryan A Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, and 1 others. 2024b. Personalization of [large language models: A survey](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/). arXiv preprint arXiv:2411.00027.
80. Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, and Kaixiang Lin. 2025a. Do llms recognize your preferences? evaluating personalized preference following in llms. In The Thirteenth International Conference on Learning Representations, ICLR 2025, 2025. [find](https://scholar.google.com/scholar?q=Siyan+Zhao%2C+Mingyi+Hong%2C+Yang+Liu%2C+Devamanyu+Hazarika%2C+and+Kaixiang+Lin%2E+2025a%2E+Do+llms+recognize+your+preferences%3F+evaluating+personalized+preference+following+in+llms%2E+In+The+Thirteenth+Internationa)
81. Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, and Ting Liu. 2025b. [Beware of your po! measuring and mitigating AI safety risks in role-play fine-tuning of LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2025-beware-your-po-measuring-mitigating/). In Proceedings of the 63rd Annual Meeting of the Association for…
82. Weixiang Zhao, Xingyu Sui, Yulin Hu, Jiahe Guo, Haix-iao Liu, Biye Li, Yanyan Zhao, Bing Qin, and Ting Liu. 2025c. Teaching language models to evolve with users: Dynamic profile modeling for personalized alignment. arXiv preprint arXiv:2505.15456. — [arXiv:2505.15456](https://arxiv.org/abs/2505.15456) [find](https://scholar.google.com/scholar?q=Weixiang+Zhao%2C+Xingyu+Sui%2C+Yulin+Hu%2C+Jiahe+Guo%2C+Haix%2Diao+Liu%2C+Biye+Li%2C+Yanyan+Zhao%2C+Bing+Qin%2C+and+Ting+Liu%2E+2025c%2E+Teaching+language+models+to+evolve+with+users%3A+Dynamic+profile+modeling+for+personali)
83. Xiaoyan Zhao, Yang Deng, Wenjie Wang, Hong Cheng, Rui Zhang, See-Kiong Ng, Tat-Seng Chua, and 1 others. 2025d. Exploring the impact of personality traits on conversational recommender systems: A simulation with large language models. arXiv preprint arXiv:2504.12313. — [arXiv:2504.12313](https://arxiv.org/abs/2504.12313) [find](https://scholar.google.com/scholar?q=Xiaoyan+Zhao%2C+Yang+Deng%2C+Wenjie+Wang%2C+Hong+Cheng%2C+Rui+Zhang%2C+See%2DKiong+Ng%2C+Tat%2DSeng+Chua%2C+and+1+others%2E+2025d%2E+Exploring+the+impact+of+personality+traits+on+conversational+recommender+systems%3A+A+simul)
84. Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, and Ruifeng Xu. 2024a. A comprehensive survey on relation extraction: Recent advances and new frontiers. ACM Computing Surveys, 56(11):1–39. [find](https://scholar.google.com/scholar?q=Xiaoyan+Zhao%2C+Yang+Deng%2C+Min+Yang%2C+Lingzhi+Wang%2C+Rui+Zhang%2C+Hong+Cheng%2C+Wai+Lam%2C+Ying+Shen%2C+and+Ruifeng+Xu%2E+2024a%2E+A+comprehensive+survey+on+relation+extraction%3A+Recent+advances+and+new+frontiers%2E+ACM)
85. Xiaoyan Zhao, Lingzhi Wang, Zhanghao Wang, Hong Cheng, Rui Zhang, and Kam-Fai Wong. 2024b. Pacar: Automated fact-checking with planning and customized action reasoning using large language models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resour… [find](https://scholar.google.com/scholar?q=Xiaoyan+Zhao%2C+Lingzhi+Wang%2C+Zhanghao+Wang%2C+Hong+Cheng%2C+Rui+Zhang%2C+and+Kam%2DFai+Wong%2E+2024b%2E+Pacar%3A+Automated+fact%2Dchecking+with+planning+and+customized+action+reasoning+using+large+language+models%2E+In+)
86. Xiaoyan Zhao, Juntao You, Yang Zhang, Wenjie Wang, Hong Cheng, Fuli Feng, See-Kiong Ng, and Tat-Seng Chua. 2025e. Nextquill: Causal preference modeling for enhancing llm personalization. arXiv preprint arXiv:2506.02368. — [arXiv:2506.02368](https://arxiv.org/abs/2506.02368) [find](https://scholar.google.com/scholar?q=Xiaoyan+Zhao%2C+Juntao+You%2C+Yang+Zhang%2C+Wenjie+Wang%2C+Hong+Cheng%2C+Fuli+Feng%2C+See%2DKiong+Ng%2C+and+Tat%2DSeng+Chua%2E+2025e%2E+Nextquill%3A+Causal+preference+modeling+for+enhancing+llm+personalization%2E+arXiv+preprin)
87. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). In Advances in Neural Information Processing Systems 36: A…
88. Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, and Bo Dai. 2024. Hydra: Model factorization framework for black-box llm personalization. In Advances in Neural Information Processing Systems, volume 37, pages 100783–100815. [find](https://scholar.google.com/scholar?q=Yuchen+Zhuang%2C+Haotian+Sun%2C+Yue+Yu%2C+Rushi+Qiang%2C+Qifan+Wang%2C+Chao+Zhang%2C+and+Bo+Dai%2E+2024%2E+Hydra%3A+Model+factorization+framework+for+black%2Dbox+llm+personalization%2E+In+Advances+in+Neural+Information+Pro)

*Updated 31 October 2026*