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Large language models (LLMs) often benefit from intermediate steps of reasoning to generate answers to complex problems.
Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I. (2019) · 1908
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019) · 1909
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Optimal policies tend to seek power
Turner, A. M., Smith, L., Shah, R., Critch, A., and Tadepalli, P. (2019) · 1912
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Information hiding-a survey
Petitcolas, F. A., Anderson, R. J., and Kuhn, M. G. (1999) · 1999
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · 2016
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Cyclegan, a master of steganography
Chu, C., Zhmoginov, A., and Sandler, M. (2017) · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P. (2017) · 2017
Earlier work this paper cites.
A review on text steganography techniques
Majeed, M. A., Sulaiman, R., Shukur, Z., and Hasan, M. K. (2021) · 2021
Earlier work this paper cites.
Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al. (2022) · 2022
Cited alongside, same era.
Discovering language model behaviors with model-written evaluations
Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · 2022
Cited alongside, same era.
Steganography in chain of thought reasoning
Ray, A. (2022) · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022) · 2022
Cited alongside, same era.
Anthropics responsible scaling policy
Anthropic (2023) · 2023
Cited alongside, same era.
Alpacaeval: An automatic evaluator of instruction-following models
Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., Liang, P., and Hashimoto, T. B. (2023) · 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) · 2023
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Gpt-4 technical report
OpenAI, R. (2023) · 2023
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Question decomposition improves the faithfulness of model-generated reasoning
Radhakrishnan, A., Nguyen, K., Chen, A., Chen, C., Denison, C., Hernandez, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., et al. (2023) · 2023
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Llms are (mostly) not helped by filler tokens
Sachan, K. (2023) · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Fernandez, P., Chaffin, A., Tit, K., Chappelier, V., and Furon, T. (2023) · 2023
Cited alongside, same era.
Comment on by default, gpts think in plain sight
Gwern (2023) · 2023
Cited alongside, same era.
Measuring faithfulness in chain-of-thought reasoning
Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., et al. (2023) · 2023
Cited alongside, same era.
A watermark for large language models
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., and Goldstein, T. (2023a)
Cited in the paper.
On the reliability of watermarks for large language models
Kirchenbauer, J., Geiping, J., Wen, Y., Shu, M., Saifullah, K., Kong, K., Fernando, K., Saha, A., Goldblum, M., and Goldstein, T. (2023b)
Cited in the paper.
A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. (2023a)
Cited in the paper.
Towards codable text watermarking for large language models
Wang, L., Yang, W., Chen, D., Zhou, H., Lin, Y., Meng, F., Zhou, J., and Sun, X. (2023b)
Cited in the paper.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · 2023
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Simple synthetic data reduces sycophancy in large language models
Wei, J., Huang, D., Lu, Y., Zhou, D., and Le, Q. V. (2023) · 2023
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Robust multi-bit natural language watermarking through invariant features
Yoo, K., Ahn, W., Jang, J., and Kwak, N. (2023) · 2092
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