Participants: Hyeseung Lee, Jaehoon Kim, Jihyeong Choi, Soyeon Park (alphabet order)

Reviewing the contents and the referred papers

[1] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023, January). ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR).

[2] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.

[3] Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., ... & Zhou, D. Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations.

[4] Diao, S., Wang, P., Lin, Y., Pan, R., Liu, X., & Zhang, T. (2023). Active prompting with chain-of-thought for large language models. arXiv preprint arXiv:2302.12246.

[5] Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., & Smola, A. (2023). Multimodal chain-of-thought reasoning in language models. arXiv preprint arXiv:2302.00923.

[6] Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., & Narasimhan, K. (2024). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36.

[7] Long, J. (2023). Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291.

[8] Xie, S. M., & Min, S. (2022). How does in-context learning work? A framework for understanding the differences from traditional supervised learning. (blog)