Fetching the paper…
Reading the bibliography…
We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell,
2020
Earlier work this paper cites.
T. Schuster, A. Kalyan, A. Polozov, and A. T. Kalai, “Programming puzzles,” in
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang, “Glm: General language model pretraining with autoregressive blank infilling,” in
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou,
2022
Earlier work this paper cites.
X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” in
2022
Earlier work this paper cites.
Z. Zhang, A. Zhang, M. Li, and A. Smola, “Automatic chain of thought prompting in large language models,” in
2022
Earlier work this paper cites.
D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. V. Le,
2022
Earlier work this paper cites.
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driessche, J.-B. Lespiau, B. Damoc, A. Clark,
2022
Earlier work this paper cites.
Z. Wang, W. Nie, Z. Qiao, C. Xiao, R. Baraniuk, and A. Anandkumar, “Retrieval-based controllable molecule generation,” in
2022
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,”
2022
Earlier work this paper cites.
S. Arora, A. Narayan, M. F. Chen, L. Orr, N. Guha, K. Bhatia, I. Chami, and C. Re, “Ask me anything: A simple strategy for prompting language models,” in
2022
Earlier work this paper cites.
T. Khot, H. Trivedi, M. Finlayson, Y. Fu, K. Richardson, P. Clark, and A. Sabharwal, “Decomposed prompting: A modular approach for solving complex tasks,” in
2022
Earlier work this paper cites.
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler,
2022
Earlier work this paper cites.
F. Shi, M. Suzgun, M. Freitag, X. Wang, S. Srivats, S. Vosoughi, H. W. Chung, Y. Tay, S. Ruder, D. Zhou,
2022
Earlier work this paper cites.
Y. Fu, H. Peng, A. Sabharwal, P. Clark, and T. Khot, “Complexity-based prompting for multi-step reasoning,” in
2022
Earlier work this paper cites.
J. Chen, R. Xu, Z. Fu, W. Shi, Z. Li, X. Zhang, C. Sun, L. Li, Y. Xiao, and H. Zhou, “E-kar: A benchmark for rationalizing natural language analogical reasoning,” in
2022
Earlier work this paper cites.
O. Sultan and D. Shahaf, “Life is a circus and we are the clowns: Automatically finding analogies between situations and processes,” in
2022
Earlier work this paper cites.
N. Zhang, L. Li, X. Chen, X. Liang, S. Deng, and H. Chen, “Multimodal analogical reasoning over knowledge graphs,” in
2022
Earlier work this paper cites.
B. Bhavya, J. Xiong, and C. Zhai, “Analogy generation by prompting large language models: A case study of instructgpt,” in
2022
Cited alongside, same era.
Z. Zhang, A. Zhang, M. Li, and A. Smola, “Automatic chain of thought prompting in large language models,” in
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
W. Chen, X. Ma, X. Wang, and W. W. Cohen, “Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks,”
2023
Later among the works it cites.
X. Ning, Z. Lin, Z. Zhou, Z. Wang, H. Yang, and Y. Wang, “Skeleton-of-thought: Large language models can do parallel decoding,” in
2023
Later among the works it cites.
Y. Zhang, “Meta prompting for agi systems,”
2023
Later among the works it cites.
B. Bhavya, J. Xiong, and C. Zhai, “Cam: A large language model-based creative analogy mining framework,” in
2023
Later among the works it cites.
T. Webb, K. J. Holyoak, and H. Lu, “Emergent analogical reasoning in large language models,”
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
L. Gao, A. Madaan, S. Zhou, U. Alon, P. Liu, Y. Yang, J. Callan, and G. Neubig, “Pal: Program-aided language models,” in
2023
Cited alongside, same era.
A. Asai, S. Min, Z. Zhong, and D. Chen, “Retrieval-based language models and applications,” in
2023
Cited alongside, same era.
G. Mialon, R. Dessi, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Roziere, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz,
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
B. bench authors, “Beyond the imitation game: Quantifying and extrapolating the capabilities of language models,”
2023
Later among the works it cites.
A. T. K. Patrick Haluptzok, Matthew Bowers, “Language models can teach themselves to program better,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
M. Yasunaga, X. Chen, Y. Li, P. Pasupat, J. Leskovec, P. Liang, E. H. Chi, and D. Zhou, “Large language models as analogical reasoners,”
2024
Closest in time.
S. Yao, D. Yu, J. Zhao, I. Shafran, T. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,”
2024
Closest in time.
2024
Closest in time.
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk,
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Yu, R. He, and Z. Ying, “Thought propagation: An analogical approach to complex reasoning with large language models,” in
2024
Closest in time.
T. Feng, P. Han, G. Lin, G. Liu, and J. You, “Thought-retriever: Don’t just retrieve raw data, retrieve thoughts,” in
2024
Closest in time.