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DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process

Paper · 2025 · Minjun Zhu, Yixuan Weng, Linyi Yang et al.

Summary

Automated paper reviewing with LLMs often produces shallow critiques. It lacks evidence, is easily swayed by manipulative prompts, and suffers from limited domain knowledge and hallucination. The authors argue that part of the cause is that open review corpora mostly contain final reviews and scores, not the reasoning that produced them. They propose DeepReview, a staged framework that imitates how an expert works. First it checks novelty by generating questions, retrieving related literature and reranking it. Next it simulates several reviewers along multiple dimensions. It then verifies each criticism against evidence in the paper and finally composes a meta-review with scores. A synthesis pipeline built this way over ICLR 2024–2025 submissions yields the DeepReview-13K training set and the DeepReview-Bench test set. On this data they fine-tune DeepReviewer-14B, which offers Fast, Standard and Best inference modes that trade compute for depth. The model beats much larger systems on rating prediction, ranking and accept/reject selection. It also holds up reasonably well against adversarial manipulation without dedicated training. All artifacts are released openly, which gives the community a reusable benchmark for assisted peer review.

Key points

  • Data:
    • The authors gathered 18,976 ICLR submissions from OpenReview and arXiv and converted them to Markdown with MinerU, preferring LaTeX sources where available.
    • DeepReview-13K holds 13,378 training papers (4,131 from ICLR 2024 and 9,247 from ICLR 2025).
    • DeepReview-Bench holds 1,286 test papers.
  • Models: DeepReviewer-14B (plus a 7B variant) outperforms CycleReviewer-70B while generating fewer output tokens, even in Fast mode.
  • Win rates: In LLM-as-a-judge comparisons, the Best mode wins 88.21% of the time against GPT-o1 and 80.20% against DeepSeek-R1.
  • Quantitative gains over baselines:
    • Rating MSE improves by about 44.80%.
    • Rating Spearman correlation improves by 6.04%.
    • Selection accuracy improves by 1.80%.
  • Limitations:
    • The training data is synthetic.
    • Best mode, which uses retrieval and the full reasoning chain, is costly to run.
    • Resistance to adversarial attacks is only partial.
    • The authors position the tool as reviewer assistance, not a replacement for human reviewers.

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

🎓Zhejiang UniversityHangzhou · China236 works · 14,072 citations🎓Westlake UniversityHangzhou · China60 works · 8,829 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksInformation Systems · 1,888 worksHistory and Philosophy of Science · 83 works

Cite

Cite as Minjun Zhu, Yixuan Weng, Linyi Yang and Yue Zhang. 2025. DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process. arXiv:2503.08569.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2503.08569 · arXiv 2503.08569

@misc{zhu2025deepreview,
  title = {{DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process}},
  author = {Minjun Zhu, Yixuan Weng, Linyi Yang and Yue Zhang},
  year = {2025},
  doi = {10.48550/arxiv.2503.08569},
  eprint = {2503.08569},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2503.08569}
}

References

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

  1. M. Abdin, J. Aneja, H. Behl, S. Bubeck, R. Eldan, S. Gunasekar, M. Harrison, R. J. Hewett, M. Javaheripi, P. Kauffmann, J. R. Lee, Y. T. Lee, Y. Li, W. Liu, C. C. T. Mendes, A. Nguyen, E. Price, G. de Rosa, O. Saarikivi, A. Salim, S. Shah, X. Wang, R. Ward, Y. Wu, D. Yu, C. Zhang, and Y. Zhang. Phi-… — Phi-4 Technical Report
  2. J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  3. A. AI. Aider is ai pair programming in your terminal. https://github.com/Aider-AI/aider, 2025. ↗
  4. E. Akyürek, D. Schuurmans, J. Andreas, T. Ma, and D. Zhou. What learning algorithm is in-context learning? investigations with linear models. arXiv preprint arXiv:2211.15661, 2022.
  5. B. Alberts, B. Hanson, and K. L. Kelner. Reviewing peer review, 2008. find
  6. A. Asai, J. He, R. Shao, W. Shi, A. Singh, J. C. Chang, K. Lo, L. Soldaini, S. Feldman, M. D’arcy, D. Wadden, M. Latzke, M. Tian, P. Ji, S. Liu, H. Tong, B. Wu, Y. Xiong, L. Zettlemoyer, G. Neubig, D. Weld, D. Downey, W. tau Yih, P. W. Koh, and H. Hajishirzi. Openscholar: Synthesizing scientific lit… — OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
  7. J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
  8. I. Blog. Iclr 2025: Assisting reviewers. https://blog.iclr.cc/2024/10/09/iclr2025-assisting-reviewers/, 2024. Accessed: 2024-10-09. ↗
  9. L. Chris, L. Cong, L. Robert, Tjarko, F. Jakob, C. Jeff, and H. David. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292v3, 2024. URL https://www.arxiv.org/abs/2408.06292v3. ↗
  10. B. Daniil, A., M. Robert, and G. Gabe. Emergent autonomous scientific research capabilities of large language models. arXiv preprint arXiv:2304.05332v1, 2023. URL https://www.arxiv.org/abs/2304.05332v1. ↗
  11. M. D’Arcy, T. Hope, L. Birnbaum, and D. Downey. Marg: Multi-agent review generation for scientific papers. arXiv preprint arXiv:2401.04259, 2024.
  12. Y. Ding, L. L. Zhang, C. Zhang, Y. Xu, N. Shang, J. Xu, F. Yang, and M. Yang. LongroPE: Extending LLM context window beyond 2 million tokens. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=ONOtpXLqqw. ↗
  13. I. Drori and D. Te’eni. Human-in-the-loop ai reviewing: Feasibility, opportunities, and risks. Journal of the Association for Information Systems, 25(1):98–109, 2024. find
  14. J. Du, Y. Wang, W. Zhao, Z. Deng, S. Liu, R. Lou, H. P. Zou, P. Narayanan Venkit, N. Zhang, M. Srinath, H. R. Zhang, V. Gupta, Y. Li, T. Li, F. Wang, Q. Liu, T. Liu, P. Gao, C. Xia, C. Xing, C. Jiayang, Z. Wang, Y. Su, R. S. Shah, R. Guo, J. Gu, H. Li, K. Wei, Z. Wang, L. Cheng, S. Ranathunga, M. Fa… — LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
  15. M. Funkquist, I. Kuznetsov, Y. Hou, and I. Gurevych. Citebench: A benchmark for scientific citation text generation. arXiv preprint arXiv:2212.09577, 2022. find
  16. Z. Gao, K. Brantley, and T. Joachims. Reviewer2: Optimizing review generation through prompt generation. arXiv preprint arXiv:2402.10886, 2024.
  17. A. Ghafarollahi and M. J. Buehler. Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning. arXiv preprint arXiv:2409.05556, 2024.
  18. X. Guan, L. L. Zhang, Y. Liu, N. Shang, Y. Sun, Y. Zhu, F. Yang, and M. Yang. rstar-math: Small llms can master math reasoning with self-evolved deep thinking, 2025. URL https://arxiv.org/abs/2501.04519. ↗
  19. D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
  20. S. Hao, S. Sukhbaatar, D. Su, X. Li, Z. Hu, J. Weston, and Y. Tian. Training large language models to reason in a continuous latent space, 2024. URL https://arxiv.org/abs/2412.06769. ↗
  21. D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Measuring mathematical problem solving with the math dataset. NeurIPS, 2021.
  22. X. Hu, H. Fu, J. Wang, Y. Wang, Z. Li, R. Xu, Y. Lu, Y. Jin, L. Pan, and Z. Lan. Nova: An iterative planning and search approach to enhance novelty and diversity of llm generated ideas. arXiv preprint arXiv:2410.14255, 2024.
  23. A. Jaech, A. Kalai, A. Lerer, A. Richardson, A. El-Kishky, A. Low, A. Helyar, A. Madry, A. Beutel, A. Carney, et al. Openai o1 system card. arXiv preprint arXiv:2412.16720, 2024.
  24. Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, and P. Fung. Towards mitigating LLM hallucination via self reflection. In H. Bouamor, J. Pino, and K. Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1827–1843, Singapore, Dec. 2023. Association for Computational Ling…
  25. Y. Jin, Q. Zhao, Y. Wang, H. Chen, K. Zhu, Y. Xiao, and J. Wang. AgentReview: Exploring peer review dynamics with LLM agents. In Y. Al-Onaizan, M. Bansal, and Y.-N. Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1208–1226, Miami, Florida,…
  26. Y. Jin, Q. Zhao, Y. Wang, H. Chen, K. Zhu, Y. Xiao, and J. Wang. Agentreview: Exploring peer review dynamics with llm agents. arXiv preprint arXiv:2406.12708, 2024b.
  27. D. Kang, W. Ammar, B. Dalvi, M. Van Zuylen, S. Kohlmeier, E. Hovy, and R. Schwartz. A dataset of peer reviews (peerread): Collection, insights and nlp applications. arXiv preprint arXiv:1804.09635, 2018.
  28. P. Langley. Scientific discovery: Computational explorations of the creative processes. MIT press, 1987. find
  29. G. R. Latona, M. H. Ribeiro, T. R. Davidson, V. Veselovsky, and R. West. The ai review lottery: Widespread ai-assisted peer reviews boost paper scores and acceptance rates. arXiv preprint arXiv:2405.02150, 2024.
  30. D. Li, B. Jiang, L. Huang, A. Beigi, C. Zhao, Z. Tan, A. Bhattacharjee, Y. Jiang, C. Chen, T. Wu, K. Shu, L. Cheng, and H. Liu. From generation to judgment: Opportunities and challenges of llm-as-a-judge. arXiv preprint arXiv: 2411.16594, 2024a. find
  31. M. Li, E. Hovy, and J. H. Lau. Summarizing multiple documents with conversational structure for meta-review generation. arXiv preprint arXiv:2305.01498, 2023. find
  32. M. Y. Li, E. Fox, and N. Goodman. Automated statistical model discovery with language models. In Forty-first International Conference on Machine Learning, 2024b. URL https://openreview.net/forum?id=B5906M4Wnd. ↗
  33. Z. Li, Y. Chang, and X. Le. Simulating expert discussions with multi-agent for enhanced scientific problem solving. In T. Ghosal, A. Singh, A. Waard, P. Mayr, A. Naik, O. Weller, Y. Lee, S. Shen, and Y. Qin, editors, Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), pag… find
  34. W. Liang, Z. Izzo, Y. Zhang, H. Lepp, H. Cao, X. Zhao, L. Chen, H. Ye, S. Liu, Z. Huang, et al. Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews. arXiv preprint arXiv:2403.07183, 2024.
  35. C.-Y. Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain, July 2004. Association for Computational Linguistics. URL https://aclanthology.org/W04-1013/. ↗
  36. A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36, 2024. find
  37. M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, et al. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.00114, 2021.
  38. K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, page 311–318, USA, 2002. Association for Computational Linguistics. doi: 10.3115/1073083.10…
  39. Qwen, :, A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, H. Lin, J. Yang, J. Tu, J. Zhang, J. Yang, J. Yang, J. Zhou, J. Lin, K. Dang, K. Lu, K. Bao, K. Yang, L. Yu, M. Li, M. Xue, P. Zhang, Q. Zhu, R. Men, R. Lin, T. Li, T. Tang, T. Xia, X. Ren, X. Ren, Y. Fan,… find
  40. S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He. Zero: Memory optimizations toward training trillion parameter models, 2020. URL https://arxiv.org/abs/1910.02054. ↗
  41. S. Rasal and E. Hauer. Navigating complexity: Orchestrated problem solving with multi-agent llms. arXiv preprint arXiv:2402.16713, 2024. — [arXiv:2402.16713](https://arxiv.org/abs/2402.16713) find
  42. J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He. 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, KDD ’20, page 3505–3506, New York, NY, USA, 202… — [doi:10.1145/3394486.3406703](https://doi.org/10.1145/3394486.3406703) find
  43. B. Rewina, P. Natalie, B. Sreyoshi, K. Satya, G. Alex, C. Elizabeth, I. Ikkei, T. David, C. Aman, and N. Naumaan. Potential and perils of large language models as judges of unstructured textual data. arXiv preprint arXiv:2501.08167v2, 2025. URL https://www.arxiv.org/abs/2501.08167v2. — [arXiv:2501.08167](https://arxiv.org/abs/2501.08167) ↗
  44. S. K. K. Santu, S. K. Sinha, N. Bansal, A. Knipper, S. Sarkar, J. Salvador, Y. Mahajan, S. Guttikonda, M. Akter, M. Freestone, et al. Prompting llms to compose meta-review drafts from peer-review narratives of scholarly manuscripts. arXiv preprint arXiv:2402.15589, 2024. — [arXiv:2402.15589](https://arxiv.org/abs/2402.15589) find
  45. D. Scherbakov, N. Hubig, V. Jansari, A. Bakumenko, and L. A. Lenert. The emergence of large language models (llm) as a tool in literature reviews: an llm automated systematic review, 2024. URL https://arxiv.org/abs/2409.04600. — [arXiv:2409.04600](https://arxiv.org/abs/2409.04600) ↗
  46. L. A. Schintler, C. L. McNeely, and J. Witte. A critical examination of the ethics of ai-mediated peer review, 2023. URL https://arxiv.org/abs/2309.12356. — [arXiv:2309.12356](https://arxiv.org/abs/2309.12356) ↗
  47. C. Si, D. Yang, and T. Hashimoto. Can LLMs generate novel research ideas? a large-scale human study with 100+ NLP researchers. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=M23dTGWCZy. ↗
  48. H. Su, R. Chen, S. Tang, X. Zheng, J. Li, Z. Yin, W. Ouyang, and N. Dong. Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation. arXiv preprint arXiv:2410.09403, 2024. — [arXiv:2410.09403](https://arxiv.org/abs/2410.09403) find
  49. S. Swarnadeep, L. Xian, G. Marjan, W. Jason, and W. Tianlu. Learning to plan & reason for evaluation with thinking-llm-as-a-judge. arXiv preprint arXiv:2501.18099v1, 2025. URL https://www.arxiv.org/abs/2501.18099v1. — [arXiv:2501.18099](https://arxiv.org/abs/2501.18099) ↗
  50. C. Tan, D. Lyu, S. Li, Z. Gao, J. Wei, S. Ma, Z. Liu, and S. Z. Li. Peer review as a multi-turn and long-context dialogue with role-based interactions. arXiv preprint arXiv:2406.05688, 2024a. — [arXiv:2406.05688](https://arxiv.org/abs/2406.05688) find
  51. C. Tan, D. Lyu, S. Li, Z. Gao, J. Wei, S. Ma, Z. Liu, and S. Z. Li. Peer review as a multi-turn and long-context dialogue with role-based interactions, 2024b. URL https://arxiv.org/abs/2406.05688. — [arXiv:2406.05688](https://arxiv.org/abs/2406.05688) ↗
  52. H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  53. K. Tyser, B. Segev, G. Longhitano, X.-Y. Zhang, Z. Meeks, J. Lee, U. Garg, N. Belsten, A. Shporer, M. Udell, et al. Ai-driven review systems: evaluating llms in scalable and bias-aware academic reviews. arXiv preprint arXiv:2408.10365, 2024.
  54. B. Wang, C. Xu, X. Zhao, L. Ouyang, F. Wu, Z. Zhao, R. Xu, K. Liu, Y. Qu, F. Shang, B. Zhang, L. Wei, Z. Sui, W. Li, B. Shi, Y. Qiao, D. Lin, and C. He. Mineru: An open-source solution for precise document content extraction, 2024a. URL https://arxiv.org/abs/2409.18839. ↗
  55. Q. Wang, Q. Zeng, L. Huang, K. Knight, H. Ji, and N. F. Rajani. ReviewRobot: Explainable paper review generation based on knowledge synthesis. In B. Davis, Y. Graham, J. Kelleher, and Y. Sripada, editors, Proceedings of the 13th International Conference on Natural Language Generation, pages 384–397,… find
  56. X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023.
  57. Y. Wang, Z. Yu, W. Yao, Z. Zeng, L. Yang, C. Wang, H. Chen, C. Jiang, R. Xie, J. Wang, X. Xie, W. Ye, S. Zhang, and Y. Zhang. PandaLM: An automatic evaluation benchmark for LLM instruction tuning optimization. In The Twelfth International Conference on Learning Representations, 2024b. URL https://op…
  58. J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. H. Chi, Q. V. Le, D. Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, 2022.
  59. Y. Weng, M. Zhu, F. Xia, B. Li, S. He, S. Liu, B. Sun, K. Liu, and J. Zhao. Large language models are better reasoners with self-verification. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
  60. Y. Weng, M. Zhu, G. Bao, H. Zhang, J. Wang, Y. Zhang, and L. Yang. Cycleresearcher: Improving automated research via automated review. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=bjcsVLoHYs. ↗
  61. V. Xiang, C. Snell, K. Gandhi, A. Albalak, A. Singh, C. Blagden, D. Phung, R. Rafailov, N. Lile, D. Mahan, L. Castricato, J.-P. Franken, N. Haber, and C. Finn. Towards system 2 reasoning in llms: Learning how to think with meta chain-of-thought, 2025. URL https://arxiv.org/abs/2501.04682. ↗
  62. Z. Yang, X. Du, J. Li, J. Zheng, S. Poria, and E. Cambria. Large language models for automated open-domain scientific hypotheses discovery. In L.-W. Ku, A. Martins, and V. Srikumar, editors, Findings of the Association for Computational Linguistics: ACL 2024, pages 13545–13565, Bangkok, Thailand, Au…
  63. S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, and K. Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 2024. find
  64. R. Ye, X. Pang, J. Chai, J. Chen, Z. Yin, Z. Xiang, X. Dong, J. Shao, and S. Chen. Are we there yet? revealing the risks of utilizing large language models in scholarly peer review. arXiv preprint arXiv:2412.01708, 2024.
  65. J. Yu, Z. Ding, J. Tan, K. Luo, Z. Weng, C. Gong, L. Zeng, R. Cui, C. Han, Q. Sun, et al. Automated peer reviewing in paper sea: Standardization, evaluation, and analysis. arXiv preprint arXiv:2407.12857, 2024. find
  66. W. Yuan, P. Liu, and G. Neubig. Can we automate scientific reviewing?, 2021. URL https://arxiv.org/abs/2102.00176. ↗
  67. Q. Zeng, M. Sidhu, H. P. Chan, L. Wang, and H. Ji. Scientific opinion summarization: Paper meta-review generation dataset, methods, and evaluation. In 1st AI4Research Workshop, 2024. find
  68. R. Zhou, L. Chen, and K. Yu. Is LLM a reliable reviewer? a comprehensive evaluation of LLM on automatic paper reviewing tasks. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, and N. Xue, editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language…
  69. R. Zhou, L. Chen, and K. Yu. Is llm a reliable reviewer? a comprehensive evaluation of llm on automatic paper reviewing tasks. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9340–9351, 2024b.
  70. Z. Zhuang, J. Chen, H. Xu, Y. Jiang, and J. Lin. Large language models for automated scholarly paper review: A survey. arXiv preprint arXiv:2501.10326, 2025. — [arXiv:2501.10326](https://arxiv.org/abs/2501.10326) find
  71. Y. Zonglin, D. Xinya, L. Junxian, Z. Jie, P. Soujanya, and C. Erik. Large language models for automated open-domain scientific hypotheses discovery. arXiv preprint arXiv:2309.02726, 2023. URL https://www.arxiv.org/abs/2309.02726. ↗
  72. D. Zyska, N. Dycke, J. Buchmann, I. Kuznetsov, and I. Gurevych. Care: Collaborative ai-assisted reading environment. arXiv preprint arXiv:2302.12611, 2023. find

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