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image https://stackoverflow.blog/2023/10/18/retrieval-augmented-generation-keeping-llms-relevant-and-current/

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  • Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022). Large language models can self-improve. arXiv preprint arXiv:2210.11610. https://arxiv.org/pdf/2210.11610.pdf]]%3E
  • 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. https://arxiv.org/pdf/2302.00923.pdf
  • Zhang, Z., Zhang, A., Li, M., & Smola, A. (2022). Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493. https://arxiv.org/pdf/2210.03493.pdf
  • Peng, Baolin, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang et al. "Check your facts and try again: Improving large language models with external knowledge and automated feedback." arXiv preprint arXiv:2302.12813 (2023). https://arxiv.org/pdf/2302.12813.pdf
  • Kaddour, Jean, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. "Challenges and applications of large language models." arXiv preprint arXiv:2307.10169 (2023). https://arxiv.org/pdf/2307.10169.pdf
  • Du, Yilun, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. "Improving Factuality and Reasoning in Language Models t hrough Multiagent Debate." arXiv preprint arXiv:2305.14325 (2023).
  • Wei, Jason, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. "Chain-of-thought prompting elicits reasoning in large language models." Advances in Neural Information Processing Systems 35 (2022): 24824-24837. https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf
  • Zhang, Zhuosheng, Aston Zhang, Mu Li, and Alex Smola. "Automatic chain of thought prompting in large language models." arXiv preprint arXiv:2210.03493 (2022). https://arxiv.org/pdf/2210.03493.pdf
  • Wang, Xuezhi, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. "Self-consistency improves chain of thought reasoning in language models." arXiv preprint arXiv:2203.11171 (2022). https://arxiv.org/pdf/2203.11171.pdf
  • Zhang, Zhuosheng, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. "Multimodal chain-of-thought reasoning in language models." arXiv preprint arXiv:2302.00923 (2023). https://arxiv.org/pdf/2302.00923.pdf
  • Suzgun, Mirac, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery et al. "Challenging big-bench tasks and whether chain-of-thought can solve them." arXiv preprint arXiv:2210.09261 (2022). https://arxiv.org/pdf/2210.09261.pdf
  • Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. arXiv preprint arXiv:2305.04388. https://arxiv.org/pdf/2305.04388.pdf
  • Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K. W., Zhu, S. C., ... & Kalyan, A. (2022). Learn to explain: Multimodal reasoning via thought chains for science question answering. Advances in Neural Information Processing Systems, 35, 2507-2521. https://proceedings.neurips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf

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