Large Language Models

LLMs

By Qiqi Duan
  • Hayes, T., Rao, R., Akin, H., Sofroniew, N.J., Oktay, D., Lin, Z., Verkuil, R., Tran, V.Q., Deaton, J., Wiggert, M. and Badkundri, R., 2025. Simulating 500 million years of evolution with a language model. Science, p.eads0018.
  • Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C.T., Allen, C., Schad, D., Wulff, D., West, J.D., Zhang, Q. and Shiffrin, R.M., 2025. How should the advancement of large language models affect the practice of science?. Proceedings of the National Academy of Sciences, 122(5), p.e2401227121.
  • Cao, B., Cai, D., Zhang, Z., Zou, Y. and Lam, W., 2025. On the worst prompt performance of large language models. Advances in Neural Information Processing Systems, 37, pp.69022-69042.
  • Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R. and Gal, Y., 2024. AI models collapse when trained on recursively generated data. Nature, 631(8022), pp.755-759.
  • Sclar, M., Choi, Y., Tsvetkov, Y. and Suhr, A., 2024. Quantifying language models’ sensitivity to spurious features in prompt design or: How I learned to start worrying about prompt formatting. In International Conference on Learning Representations.
  • White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J. and Schmidt, D.C., 2023. A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.
  • https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
  • Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H. and Ba, J., 2023, November. Large language models are human-level prompt engineers. In International Conference on Learning Representations.
  • Zhuge, M., Liu, H., Faccio, F., Ashley, D.R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H.A.A.K., Herrmann, V., Irie, K. and Kirsch, L., 2023. Mindstorms in natural language-based societies of mind. arXiv preprint arXiv:2305.17066.
  • Prasad, A., Hase, P., Zhou, X. and Bansal, M., 2023, May. GrIPS: Gradient-free, edit-based instruction search for prompting large language models. In Proceedings of Conference of European Chapter of the Association for Computational Linguistics (pp. 3845-3864).
  • Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A. and Hubert, T., 2022. Competition-level code generation with alphacode. Science, 378(6624), pp.1092-1097.
  • Sun, T., Shao, Y., Qian, H., Huang, X. and Qiu, X., 2022, June. Black-box tuning for language-model-as-a-service. In International Conference on Machine Learning (pp. 20841-20855). PMLR.
  • Li, X.L. and Liang, P., 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing (Volume 1: Long Papers). ACL.
  • Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. and Agarwal, S., 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, pp.1877-1901.
  • Kaplan, J., McCandlish, S., Henighan, T., Brown, T.B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J. and Amodei, D., 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I., 2019. Language models are unsupervised multitask learners. OpenAI Blog.
  • Bengio, Y., Ducharme, R., Vincent, P. and Jauvin, C., 2003. A neural probabilistic language model. Journal of Machine Learning Research, 3(Feb), pp.1137-1155.

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