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Self-awareness, i.e., the ability to assess and correct one's own generation, is a fundamental aspect of human intelligence, making its replication in large language models (LLMs) an important yet challenging task.
Catastrophic interference in connectionist networks: The sequential learning problem
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Program synthesis with large language models
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Training verifiers to solve math word problems
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Large language models can self-improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., and Han, J · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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A path towards autonomous machine intelligence
LeCun, Y · 2022
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
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Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al · 2022
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Zelikman, E., Wu, Y., Mu, J., and Goodman, N. D · 2022
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Large language models cannot self-correct reasoning yet
Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2023
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Scaling relationship on learning mathematical reasoning with large language models
Yuan, Z., Yuan, H., Li, C., Dong, G., Lu, K., Tan, C., Zhou, C., and Zhou, J · 2023
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Do llms exhibit human-like reasoning? evaluating theory of mind in llms for open-ended responses
Amirizaniani, M., Martin, E., Sivachenko, M., Mashhadi, A., and Shah, C · 2024
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The pitfalls of next-token prediction
Bachmann, G. and Nagarajan, V · 2024
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System-2 mathematical reasoning via enriched instruction tuning
Cai, H., Yang, Y., and Li, Z · 2024
System 2 reasoning capabilities are nigh
Lowe, S. C · 2024
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Improve mathematical reasoning in language models by automated process supervision
Luo, L., Liu, Y., Liu, R., Phatale, S., Lara, H., Li, Y., Shu, L., Zhu, Y., Meng, L., Sun, J., et al · 2024
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Reft: Reasoning with reinforced fine-tuning
Luong, T. Q., Zhang, X., Jie, Z., Sun, P., Jin, X., and Li, H · 2024
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Recursive introspection: Teaching language model agents how to self-improve
Qu, Y., Zhang, T., Garg, N., and Kumar, A · 2024
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Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., and Demir, I · 2024
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Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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V-star: Training verifiers for self-taught reasoners
Hosseini, A., Yuan, X., Malkin, N., Courville, A., Sordoni, A., and Agarwal, R · 2024
Cited alongside, same era.
Large language models cannot self-correct reasoning yet
Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D · 2024
Cited alongside, same era.
Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al · 2024
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Speculative decoding with big little decoder
Kim, S., Mangalam, K., Moon, S., Malik, J., Mahoney, M. W., Gholami, A., and Keutzer, K · 2024
Cited alongside, same era.
Training language models to self-correct via reinforcement learning
Kumar, A., Zhuang, V., Agarwal, R., Su, Y., Co-Reyes, J. D., Singh, A., Baumli, K., Iqbal, S., Bishop, C., Roelofs, R., Zhang, L. M., McKinney, K., Shrivastava, D., Paduraru, C., Tucker, G., Precup, D., Behbahani, F., and Faust, A · 2024
Cited alongside, same era.
Improving llm reasoning through scaling inference computation with collaborative verification
Liang, Z., Liu, Y., Niu, T., Zhang, X., Zhou, Y., and Yavuz, S · 2024
Cited alongside, same era.
Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., and Yao, S · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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Trial and error: Exploration-based trajectory optimization for llm agents
Song, Y., Yin, D., Yue, X., Huang, J., Li, S., and Lin, B. Y · 2024
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2024
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A human-like reasoning framework for multi-phases planning task with large language models
Xie, C. and Zou, D · 2024
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When search engine services meet large language models: visions and challenges
Xiong, H., Bian, J., Li, Y., Li, X., Du, M., Wang, S., Yin, D., and Helal, S · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Metamath: Bootstrap your own mathematical questions for large language models
Yu, L., Jiang, W., Shi, H., Jincheng, Y., Liu, Z., Zhang, Y., Kwok, J., Li, Z., Weller, A., and Liu, W · 2024
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