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By utilizing more computational resources at test-time, large language models (LLMs) can improve without additional training.
Social choice and individual values , volume 12
Arrow, K. J · 2012
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Social choice theory: An introduction
Kelly, J. S · 2013
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The theory of social choice
Fishburn, P. C · 2015
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Handbook of computational social choice
Brandt, F., Conitzer, V., Endriss, U., Lang, J., and Procaccia, A. D · 2016
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Irving, G., Christiano, P., and Amodei, D · 2018
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Language models are few-shot learners
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al · 2021
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Show your work: Scratchpads for intermediate computation with language models
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., et al · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
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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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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
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Self-critiquing models for assisting human evaluators
Saunders, W., Yeh, C., Wu, J., Bills, S., Ouyang, L., Ward, J., and Leike, J · 2022
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Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al · 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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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
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Socratic models: Composing zero-shot multimodal reasoning with language
Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Compositional foundation models for hierarchical planning
Ajay, A., Han, S., Du, Y., Li, S., Gupta, A., Jaakkola, T., Tenenbaum, J., Kaelbling, L., Srivastava, A., and Agrawal, P · 2023
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Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Towards reasoning in large language models via multi-agent peer review collaboration
Xu, Z., Shi, S., Hu, B., Yu, J., Li, D., Zhang, M., and Wu, Y · 2023
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Prompt-based monte-carlo tree search for goal-oriented dialogue policy planning
Yu, X., Chen, M., and Yu, Z · 2023
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Judging LLM-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Language agent tree search unifies reasoning acting and planning in language models
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Burns, C., Izmailov, P., Kirchner, J. H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al · 2023
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Chateval: Towards better LLM-based evaluators through multi-agent debate
Chan, C.-M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., Fu, J., and Liu, Z · 2023
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Reconcile: Round-table conference improves reasoning via consensus among diverse LLMs
Chen, J. C.-Y., Saha, S., and Bansal, M · 2023
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2023
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Lm vs lm: Detecting factual errors via cross examination
Cohen, R., Hamri, M., Geva, M., and Globerson, A · 2023
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Reward model ensembles help mitigate overoptimization
Coste, T., Anwar, U., Kirk, R., and Krueger, D · 2023
Cited alongside, same era.
Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Zhang, Y., Chow, W., Pan, R., Diao, S., Zhang, J., Shum, K., and Zhang, T · 2023
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Improving factuality and reasoning in language models through multiagent debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
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Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X · 2023
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Graph of thoughts: Solving elaborate problems with large language models
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., et al · 2024
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Alphamath almost zero: process supervision without process
Chen, G., Liao, M., Li, C., and Fan, K · 2024
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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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Thinking tokens for language modeling
Herel, D. and Mikolov, T · 2024
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Multi-modal and multi-agent systems meet rationality: A survey
Jiang, B., Xie, Y., Wang, X., Su, W. J., Taylor, C. J., and Mallick, T · 2024
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Tree search for language model agents
Koh, J. Y., McAleer, S., Fried, D., and Salakhutdinov, R · 2024
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Rewardbench: Evaluating reward models for language modeling
Lambert, N., Pyatkin, V., Morrison, J., Miranda, L., Lin, B. Y., Chandu, K., Dziri, N., Kumar, S., Zick, T., Choi, Y., et al · 2024
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Skywork-reward: Bag of tricks for reward modeling in llms
Liu, C. Y., Zeng, L., Liu, J., Yan, R., He, J., Wang, C., Yan, S., Liu, Y., and Zhou, Y · 2024
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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 · 2024
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Mahan, D., Van Phung, D., Rafailov, R., Blagden, C., Lile, N., Castricato, L., Fränken, J.-P., Finn, C., and Albalak, A · 2024
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Agent q: Advanced reasoning and learning for autonomous ai agents
Putta, P., Mills, E., Garg, N., Motwani, S., Finn, C., Garg, D., and Rafailov, R · 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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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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Self-evaluation guided beam search for reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, J. X., Kan, M.-Y., He, J., and Xie, M · 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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Tool learning with large language models: A survey
Qu, C., Dai, S., Wei, X., Cai, H., Wang, S., Yin, D., Xu, J., and Wen, J.-R · 2025
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Multiagent finetuning: Self improvement with diverse reasoning chains
Subramaniam, V., Du, Y., Tenenbaum, J. B., Torralba, A., Li, S., and Mordatch, I · 2025
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