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In-context learning (ICL) enables large language models (LLMs) to perform downstream tasks through advanced prompting and high-quality demonstrations.
S. Russell and E. Wefald, “Principles of metareasoning,” Artificial Intelligence
1991
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning
1997
Earlier work this paper cites.
F.-L. Lee and R. Heyworth, “Problem complexity: A measure of problem difficulty in algebra by using computer,” Education Journal
2000
Earlier work this paper cites.
L. Kocsis and C. Szepesvári, “Bandit based monte-carlo planning,” in Machine Learning: ECML 2006
2006
Earlier work this paper cites.
G. Chaslot, S. Bakkes, I. Szita, and P. Spronck, “Monte-carlo tree search: A new framework for game ai,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
2008
Earlier work this paper cites.
New York, NY: Farrar, Straus and Giroux, 2011
D. Kahneman, Thinking, Fast and Slow · 2011
Earlier work this paper cites.
M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2014
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, A. Ramesh, D. Ziegler, J. Wu, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems
2020
Earlier work this paper cites.
Springer Singapore, 1 ed., 2020
H. Dong, Z. Ding, and S. Zhang, Deep Reinforcement Learning: Fundamentals, Research and Applications · 2020
Earlier work this paper cites.
W. Ye, S. Liu, T. Kurutach, P. Abbeel, and Y. Gao, “Mastering atari games with limited data,” in Advances in Neural Information Processing Systems
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Patel, S. Bhattamishra, and N. Goyal, “Are nlp models really able to solve simple math word problems?,” in North American Chapter of the Association for Computational Linguistics
2021
Earlier work this paper cites.
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,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
2021
Earlier work this paper cites.
M. Geva, D. Khashabi, E. Segal, T. Khot, D. Roth, and J. Berant, “Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,” Transactions of the Association for Computational Linguistics
2021
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems
2022
Earlier work this paper cites.
OpenAI, “Introducing chatgpt,” November 2022
2022
Earlier work this paper cites.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
S. Hao, Y. Gu, H. Ma, J. Hong, Z. Wang, D. Wang, and Z. Hu, “Reasoning with language model is planning with world model,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
2023
Earlier work this paper cites.
Y. Fu, H. Peng, L. Ou, A. Sabharwal, and T. Khot, “Specializing smaller language models towards multi-step reasoning,” in International Conference on Machine Learning
2023
Earlier work this paper cites.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language models with self-generated instructions,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2023
Earlier work this paper cites.
L. Wang, L. Li, D. Dai, D. Chen, H. Zhou, F. Meng, J. Zhou, and X. Sun, “Label words are anchors: An information flow perspective for understanding in-context learning,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
2023
Earlier work this paper cites.
P. I. Jaffe, R. A. Poldrack, R. J. Schafer, and et al., “Modelling human behaviour in cognitive tasks with latent dynamical systems,” Nature Human Behaviour
2023
Earlier work this paper cites.
S. Da Silva, “System 1 vs. system 2 thinking,” Psych
2023
Earlier work this paper cites.
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
Earlier work this paper cites.
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,” in Advances in Neural Information Processing Systems
2023
Cited alongside, same era.
L. Yuan, Y. Chen, G. Cui, H. Gao, F. Zou, X. Cheng, H. Ji, Z. Liu, and M. Sun, “Revisiting out-of-distribution robustness in NLP: Benchmarks, analysis, and LLMs evaluations,” in Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track
2023
Cited alongside, same era.
2024
Cited alongside, same era.
Z. Xi, W. Chen, B. Hong, S. Jin, R. Zheng, W. He, Y. Ding, S. Liu, X. Guo, J. Wang, H. Guo, W. Shen, X. Fan, Y. Zhou, S. Dou, X. Wang, X. Zhang, peng sun, T. Gui, Q. Zhang, and X. Huang, “Training large language models for reasoning through reverse curriculum reinforcement learning,” in Forty-first International Conference on Machine Learning
R. Agarwal, A. Singh, L. M. Zhang, B. Bohnet, L. Rosias, S. C. Chan, B. Zhang, A. Anand, Z. Abbas, A. Nova, J. D. Co-Reyes, E. Chu, F. Behbahani, A. Faust, and H. Larochelle, “Many-shot in-context learning,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems
2024
Closest in time.
Qwen Team, “Qwen2.5: A party of foundation models,” September 2024
2024
Closest in time.
A. Yang, B. Yang, B. Hui, B. Zheng, B. Yu, C. Zhou, C. Li, C. Li, D. Liu, F. Huang, et al
2024
Closest in time.
A. Young, B. Chen, C. Li, C. Huang, G. Zhang, G. Zhang, H. Li, J. Zhu, J. Chen, J. Chang, et al
2024
Closest in time.
J. Wang, X. Hu, W. Hou, H. Chen, R. Zheng, Y. Wang, L. Yang, W. Ye, H. Huang, X. Geng, B. Jiao, Y. Zhang, and X. Xie, “On the robustness of chatgpt: An adversarial and out-of-distribution perspective,” IEEE Data Eng. Bull
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2024
Cited alongside, same era.
J. Ahn, R. Verma, R. Lou, D. Liu, R. Zhang, and W. Yin, “Large language models for mathematical reasoning: Progresses and challenges,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
2024
Cited alongside, same era.
Y. Zhou, J. Li, Y. Xiang, H. Yan, L. Gui, and Y. He, “The mystery of in-context learning: A comprehensive survey on interpretation and analysis,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
2024
Cited alongside, same era.
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M. Luo, X. Xu, Y. Liu, P. Pasupat, and M. Kazemi, “In-context learning with retrieved demonstrations for language models: A survey,” Transactions on Machine Learning Research · 2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
F. Shi, P. Qing, D. Yang, N. Wang, Y. Lei, H. Lu, X. Lin, and D. Li, “Prompt space optimizing few-shot reasoning success with large language models,” in Findings of the Association for Computational Linguistics: NAACL 2024
2024
Cited alongside, same era.
S. Wang, Z. Chen, C. Shi, C. Shen, and J. Li, “Mixture of demonstrations for in-context learning,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems
2024
Cited alongside, same era.
A. Zhao, F. Ye, J. Fu, and X. Shen, “Unveiling in-context learning: A coordinate system to understand its working mechanism,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
2024
Cited alongside, same era.
2024
Closest in time.
H. Yang, Y. Zhang, J. Xu, H. Lu, P.-A. Heng, and W. Lam, “Unveiling the generalization power of fine-tuned large language models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
2024
Closest in time.
Y. Yang, Y. Ma, and P. Liu, “Weak-to-strong reasoning,” in Findings of the Association for Computational Linguistics: EMNLP 2024
2024
Closest in time.
2024
Closest in time.
L. Yang, Z. Yu, T. Zhang, S. Cao, M. Xu, W. Zhang, J. E. Gonzalez, and B. Cui, “Buffer of thoughts: Thought-augmented reasoning with large language models,” Advances in Neural Information Processing Systems
2024
Closest in time.
2024
Closest in time.
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, et al
2024
Closest in time.
G. Chen, M. Liao, C. Li, and K. Fan, “Alphamath almost zero: Process supervision without process,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems
2024
Closest in time.
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” Transactions of the Association for Computational Linguistics
2024
Closest in time.
S. Wu, Z. Peng, X. Du, T. Zheng, M. Liu, J. Wu, J. Ma, Y. Li, J. Yang, W. Zhou, et al
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Anthropic, “Introducing the next generation of claude,” March 2024
2024
Closest in time.
Google DeepMind, “Gemini models,” May 2024
2024
Closest in time.
2024
Closest in time.
Mistral AI team, “Cheaper, better, faster, stronger,” April 2024
2024
Closest in time.
Mistral AI team, “Large enough,” July 2024
2024
Closest in time.
2024
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
L. Yang, Z. Yu, T. Zhang, M. Xu, J. E. Gonzalez, B. CUI, and S. YAN, “Supercorrect: Advancing small LLM reasoning with thought template distillation and self-correction,” in The Thirteenth International Conference on Learning Representations
2025
Closest in time.