Built on

Computer science as empirical inquiry: symbols and search,
A. Newell, H. A. Simon, · 1976
Earlier work this paper cites.
N. J. Nilsson, The Physical Symbol System Hypothesis: Status and Prospects, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007, pp. 9–17. URL:
2007
Earlier work this paper cites.
Systematic literature reviews in software engineering – a systematic literature review,
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, S. Linkman, · 2008
Earlier work this paper cites.
Attention is all you need,
Original
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, · 2017
Earlier work this paper cites.
Improving language understanding by generative pre-training (2018)
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, · 2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Original
2019
Earlier work this paper cites.
Contrastive reinforcement learning of symbolic reasoning domains,
G. Poesia, W. Dong, N. Goodman, · 2021
Earlier work this paper cites.
H. Zhang, L. H. Li, T. Meng, K.-W. Chang, G. V. den Broeck, On the paradox of learning to reason from data, 2022
Original
2022
Earlier work this paper cites.
K. M. Collins, C. Wong, J. Feng, M. Wei, J. B. Tenenbaum, Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks, 2022
Original
2022
Earlier work this paper cites.
J. Uesato, N. Kushman, R. Kumar, F. Song, N. Siegel, L. Wang, A. Creswell, G. Irving, I. Higgins, Solving math word problems with process- and outcome-based feedback, 2022. URL:
Original
2022
Earlier work this paper cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, Y. Cao, React: Synergizing reasoning and acting in language models, 2023
Original
2023
Earlier work this paper cites.

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Then

Graph of thoughts: Solving elaborate problems with large language models,
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, T. Hoefler, · 2024
Closest in time.
Large language models are neurosymbolic reasoners,
M. Fang, S. Deng, Y. Zhang, Z. Shi, L. Chen, M. Pechenizkiy, J. Wang, · 2024
Closest in time.
Integrating language models with symbolic formulas for first-order logic reasoning,
Y. Sheng, L. Li, Y. Wang, D. Zeng, · 2024
Closest in time.
D. Cunnington, M. Law, J. Lobo, A. Russo, The role of foundation models in neuro-symbolic learning and reasoning, 2024
Original
2024
Closest in time.
F. Xu, Z. Wu, Q. Sun, S. Ren, F. Yuan, S. Yuan, Q. Lin, Y. Qiao, J. Liu, Symbol-llm: Towards foundational symbol-centric interface for large language models, 2024
Original
2024
Closest in time.

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2024

Can Large Language Models Act as Symbolic Reasoners?

Sullivan, Rob, Elsayed, Nelly

Understand

The performance of Large language models (LLMs) across a broad range of domains has been impressive but have been critiqued as not being able to reason about their process and conclusions derived.

This is to explain the conclusions draw, and also for determining a plan or strategy for their approach.
This paper explores the current research in investigating symbolic reasoning and LLMs, and whether an LLM can inherently provide some form of reasoning or whether supporting components are necessary, and, if there is evidence for a reasoning capability, is this evident in a specific domain or is this a general capability? In addition, this paper aims to identify the current research gaps and future trends of LLM explainability, presenting a review of the literature, identifying current research into this topic and suggests areas for future work.

Built on

Computer science as empirical inquiry: symbols and search,
A. Newell, H. A. Simon, · 1976
Earlier work this paper cites.
N. J. Nilsson, The Physical Symbol System Hypothesis: Status and Prospects, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007, pp. 9–17. URL:
2007
Earlier work this paper cites.
Systematic literature reviews in software engineering – a systematic literature review,
B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, S. Linkman, · 2008
Earlier work this paper cites.
Attention is all you need,
Original
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, · 2017
Earlier work this paper cites.
Improving language understanding by generative pre-training (2018)
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, · 2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Original
2019
Earlier work this paper cites.
Contrastive reinforcement learning of symbolic reasoning domains,
G. Poesia, W. Dong, N. Goodman, · 2021
Earlier work this paper cites.
H. Zhang, L. H. Li, T. Meng, K.-W. Chang, G. V. den Broeck, On the paradox of learning to reason from data, 2022
Original
2022
Earlier work this paper cites.
K. M. Collins, C. Wong, J. Feng, M. Wei, J. B. Tenenbaum, Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks, 2022
Original
2022
Earlier work this paper cites.
J. Uesato, N. Kushman, R. Kumar, F. Song, N. Siegel, L. Wang, A. Creswell, G. Irving, I. Higgins, Solving math word problems with process- and outcome-based feedback, 2022. URL:
Original
2022
Earlier work this paper cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, Y. Cao, React: Synergizing reasoning and acting in language models, 2023
Original
2023
Earlier work this paper cites.

Similar

Then

Graph of thoughts: Solving elaborate problems with large language models,
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, T. Hoefler, · 2024
Closest in time.
Large language models are neurosymbolic reasoners,
M. Fang, S. Deng, Y. Zhang, Z. Shi, L. Chen, M. Pechenizkiy, J. Wang, · 2024
Closest in time.
Integrating language models with symbolic formulas for first-order logic reasoning,
Y. Sheng, L. Li, Y. Wang, D. Zeng, · 2024
Closest in time.
D. Cunnington, M. Law, J. Lobo, A. Russo, The role of foundation models in neuro-symbolic learning and reasoning, 2024
Original
2024
Closest in time.
F. Xu, Z. Wu, Q. Sun, S. Ren, F. Yuan, S. Yuan, Q. Lin, Y. Qiao, J. Liu, Symbol-llm: Towards foundational symbol-centric interface for large language models, 2024
Original
2024
Closest in time.