Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks, yet their reasoning capabilities remain a fundamental challenge.
R. R. Yager, “Approximate reasoning as a basis for rule-based expert systems,” IEEE Transactions on Systems, Man, and Cybernetics , no. 4, pp. 636–643, 1984
1984
Earlier work this paper cites.
R. Sun, “Robust reasoning: integrating rule-based and similarity-based reasoning,” Artificial Intelligence , vol. 75, no. 2, pp. 241–295, 1995
1995
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap, “A simple neural network module for relational reasoning,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017
2017
Earlier work this paper cites.
W. L. Hamilton et al. , “Inductive representation learning on large graphs,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in International Conference on Machine Learning (ICML) , 2017, pp. 1321–1330
2017
Earlier work this paper cites.
Z. C. Lipton, “The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.” Queue , vol. 16, no. 3, pp. 31–57, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Z. Yang, P. Qi, S. Zhang, Y. Bengio, W. Cohen, R. Salakhutdinov, and C. D. Manning, “Hotpotqa: A dataset for diverse, explainable multi-hop question answering,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , 2018, pp. 2369–2380
2018
Earlier work this paper cites.
B. Lake and M. Baroni, “Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks,” in International conference on machine learning . PMLR, 2018, pp. 2873–2882
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Brown et al. , “Language models are few-shot learners,” Advances in Neural Information Processing Systems , 2020
2020
Earlier work this paper cites.
X. Zhou, Y. Zhang, L. Cui, and D. Huang, “Evaluating commonsense in pre-trained language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 9733–9740
2020
Earlier work this paper cites.
P. Lewis et al. , “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems , 2020
2020
Earlier work this paper cites.
A. Talmor, O. Tafjord, P. Clark, Y. Goldberg, and J. Berant, “Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge,” Advances in Neural Information Processing Systems , vol. 33, pp. 20 227–20 237, 2020
2020
Earlier work this paper cites.
C. Durkan, I. Murray, and G. Papamakarios, “On contrastive learning for likelihood-free inference,” in International conference on machine learning . PMLR, 2020, pp. 2771–2781
2020
Earlier work this paper cites.
Y. Nie, A. Williams, E. Dinan, M. Bansal, J. Weston, and D. Kiela, “Adversarial nli: A new benchmark for natural language understanding,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
P. Clark, O. Tafjord, and K. Richardson, “Transformers as soft reasoners over language,” in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence , 2021, pp. 3882–3890
2021
Cited alongside, same era.
K. Shuster, S. Poff, M. Chen, D. Kiela, and J. Weston, “Retrieval augmentation reduces hallucination in conversation,” in Findings of the Association for Computational Linguistics: EMNLP 2021 , 2021, pp. 3784–3803
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
A. d. Garcez and L. C. Lamb, “Neurosymbolic ai: The 3 rd wave,” Artificial Intelligence Review , vol. 56, no. 11, pp. 12 387–12 406, 2023
2023
Later among the works it cites.
L. Gao, A. Madaan, S. Zhou, U. Alon, P. Liu, Y. Yang, J. Callan, and G. Neubig, “Pal: Program-aided language models,” in International Conference on Machine Learning . PMLR, 2023, pp. 10 764–10 799
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…
2021
Cited alongside, same era.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and S. Y. Philip, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE transactions on neural networks and learning systems , vol. 33, no. 2, pp. 494–514, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the math dataset,” Sort , vol. 2, no. 4, pp. 0–6, 2021
2021
Cited alongside, same era.
O. Tafjord, B. Dalvi, and P. Clark, “Proofwriter: Generating implications, proofs, and abductive statements over natural language,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 3621–3634
2021
Cited alongside, same era.
J. Liu, L. Cui, H. Liu, D. Huang, Y. Wang, and Y. Zhang, “Logiqa: a challenge dataset for machine reading comprehension with logical reasoning,” in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence , 2021, pp. 3622–3628
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Cited alongside, same era.
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” Advances in Neural Information Processing Systems , vol. 36, pp. 68 539–68 551, 2023
2023
Later among the works it cites.
G. Mialon, R. Dessi, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Roziere, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz, E. Grave, Y. LeCun, and T. Scialom, “Augmented language models: a survey,” Transactions on Machine Learning Research , 2023, survey Certification. [Online]. Available: https://openreview.net/forum?id=jh7wH2AzKK
2023
Later among the works it cites.
2023
Later among the works it cites.
E. First, M. N. Rabe, T. Ringer, and Y. Brun, “Baldur: Whole-proof generation and repair with large language models,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 1229–1241
2023
Later among the works it cites.
M. Mitchell and D. C. Krakauer, “The debate over understanding in ai’s large language models,” Proceedings of the National Academy of Sciences , vol. 120, no. 13, p. e2215907120, 2023
2023
Later among the works it cites.
Z. Wu, L. Qiu, A. Ross, E. Akyürek, B. Chen, B. Wang, N. Kim, J. Andreas, and Y. Kim, “Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , 2024, pp. 1819–1862
2024
Later among the works it cites.
Z. Yang, L. Dong, X. Du, H. Cheng, E. Cambria, X. Liu, J. Gao, and F. Wei, “Language models as inductive reasoners,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , 2024, pp. 209–225
2024
Later among the works it cites.
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,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Later among the works it cites.
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin et al. , “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” ACM Transactions on Information Systems , 2024
2024
Later among the works it cites.
W. Wang, L. Dong, H. Cheng, X. Liu, X. Yan, J. Gao, and F. Wei, “Augmenting language models with long-term memory,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.