---
title: "Faithful Logical Reasoning via Symbolic — Xu 2024"
description: "Plain Chain-of-Thought (CoT) prompting often leads large language models into invalid deductions on logic problems, because free-form natural-language…"
authors: ["Jundong Xu", "Hao Fei", "Liangming Pan", "Qian Liu", "Mong-Li Lee", "Wynne Hsu"]
year: 2024
venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2024.acl-long.720/"
doi: "10.18653/v1/2024.acl-long.720"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2024-faithful-logical-reasoning-symbolic-chain/"
---
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# Faithful Logical Reasoning via Symbolic Chain-of-Thought

**Authors:** Jundong Xu, [Hao Fei](https://md.research.elamur.ai/free/en/authors/hao-fei/), [Liangming Pan](https://md.research.elamur.ai/free/en/authors/liangming-pan/), [Qian Liu](https://md.research.elamur.ai/free/en/authors/qian-liu/), Mong-Li Lee, Wynne Hsu · **Year:** 2024 · **Venue:** Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.acl-long.720/) · [PDF](https://aclanthology.org/2024.acl-long.720.pdf) · [DOI 10.18653/v1/2024.acl-long.720](https://doi.org/10.18653/v1/2024.acl-long.720) · [OpenAlex](https://openalex.org/W4402671211)

**Citations:** 51 citations (openalex-snapshot · 2026-10-14) · [78](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Plain Chain-of-Thought (CoT) prompting often leads large language models into invalid deductions on logic problems, because free-form natural-language rationales lack the rigor of formal inference. Earlier neuro-symbolic systems such as Logic-LM and LINC use the LLM only to turn text into logic and leave the inference to an external solver. That setup breaks on formatting mistakes, can lose information, and produces hard-to-read outputs. The authors propose SymbCoT, in which one LLM handles the whole pipeline with no external tools. A Translator turns premises into symbolic form. A Planner splits the question into sub-steps. A Solver applies explicit inference rules while keeping natural-language context. A Verifier re-checks the translation and each step against the original premises. The method was tested on five standard logical reasoning benchmarks with First-Order Logic and Constraint Optimization representations, using GPT-3.5 and GPT-4. SymbCoT consistently beats vanilla CoT and sets new state-of-the-art results, with bigger gains on harder problems. The work shows that LLMs can reason symbolically in a faithful, robust and explainable way without external provers.

## Key points
- SymbCoT has four LLM-based modules (Translator, Planner, Solver, Verifier) and uses a plan-then-solve design with retrospective verification. It mixes symbolic and natural-language expressions.
- The evaluation covers 5 datasets, two symbolic formalisms (FOL and CO) and two backbones (GPT-3.5, GPT-4). The method outperforms CoT and solver-based SoTA systems, and its advantage grows with task complexity.
- Case studies illustrate two failure modes:
  - Vanilla CoT commits "affirming the consequent" on the Descampe / 1992 du Maurier Classic question.
  - Prover9 answers "Uncertain" on the Harry/Walden example because a predicate was lost in translation, while SymbCoT correctly derives True.
- The authors list three limitations:
  - Only two symbolic languages were tested.
  - Longer reasoning chains increase token usage and API cost.
  - Effectiveness depends on the backbone LLM's own planning ability.
- The paper appeared at ACL 2024 (Long Papers). Code is available at github.com/Aiden0526/SymbCoT.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

78 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 76 not yet.

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
- [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2024faithful,
  title = {{Faithful Logical Reasoning via Symbolic Chain-of-Thought}},
  author = {Xu, Jundong and Fei, Hao and Pan, Liangming and Liu, Qian and Lee, Mong-Li and Hsu, Wynne},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.720},
  url = {https://doi.org/10.18653/v1/2024.acl-long.720}
}
```

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## References

38 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Alfonso Amayuelas, Shuai Zhang, Susie Xi Rao, and Ce Zhang. 2022. Neural methods for logical reasoning over knowledge graphs. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Alfonso+Amayuelas%2C+Shuai+Zhang%2C+Susie+Xi+Rao%2C+and+Ce+Zhang%2E+2022%2E+Neural+methods+for+logical+reasoning+over+knowledge+graphs%2E+In+Proceedings+of+the+International+Conference+on+Learning+Representations)
2. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. 2023. Graph of thoughts: Solving elaborate problems with large language models. CoRR, abs/2308.09687. [find](https://scholar.google.com/scholar?q=Maciej+Besta%2C+Nils+Blach%2C+Ales+Kubicek%2C+Robert+Gerstenberger%2C+Lukas+Gianinazzi%2C+Joanna+Gajda%2C+Tomasz+Lehmann%2C+Michal+Podstawski%2C+Hubert+Niewiadomski%2C+Piotr+Nyczyk%2C+and+Torsten+Hoefler%2E+2023%2E+Graph+of+)
3. D.D. Cummins, T. Lubart, O. Alksnis, et al. 1991. Conditional reasoning and causation. Memory & Cognition, 19:274–282. [find](https://scholar.google.com/scholar?q=D%2ED%2E+Cummins%2C+T%2E+Lubart%2C+O%2E+Alksnis%2C+et+al%2E+1991%2E+Conditional+reasoning+and+causation%2E+Memory+%26+Cognition%2C+19%3A274%E2%80%93282%2E)
4. Zackary Okun Dunivin. 2024. Scalable qualitative coding with llms: Chain-of-thought reasoning matches human performance in some hermeneutic tasks. CoRR, abs/2401.15170. — [arXiv:2401.15170](https://arxiv.org/abs/2401.15170) [find](https://scholar.google.com/scholar?q=Zackary+Okun+Dunivin%2E+2024%2E+Scalable+qualitative+coding+with+llms%3A+Chain%2Dof%2Dthought+reasoning+matches+human+performance+in+some+hermeneutic+tasks%2E+CoRR%2C+abs%2F2401%2E15170%2E+%E2%80%94+%5BarXiv%3A2401%2E15170%5D%28https%3A%2F%2Far)
5. Herbert B. Enderton. 1972. A mathematical introduction to logic. Academic Press. [find](https://scholar.google.com/scholar?q=Herbert+B%2E+Enderton%2E+1972%2E+A+mathematical+introduction+to+logic%2E+Academic+Press%2E)
6. Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, and Tat-Seng Chua. 2023. Reasoning implicit sentiment with chain-of-thought prompting. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 1171–1182. [find](https://scholar.google.com/scholar?q=Hao+Fei%2C+Bobo+Li%2C+Qian+Liu%2C+Lidong+Bing%2C+Fei+Li%2C+and+Tat%2DSeng+Chua%2E+2023%2E+Reasoning+implicit+sentiment+with+chain%2Dof%2Dthought+prompting%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Comp)
7. Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong Li Lee, and Wynne Hsu. 2024. Video-of-thought: Step-by-step video reasoning from perception to cognition. In Proceedings of the International Conference on Machine Learning, ICML. [find](https://scholar.google.com/scholar?q=Hao+Fei%2C+Shengqiong+Wu%2C+Wei+Ji%2C+Hanwang+Zhang%2C+Meishan+Zhang%2C+Mong+Li+Lee%2C+and+Wynne+Hsu%2E+2024%2E+Video%2Dof%2Dthought%3A+Step%2Dby%2Dstep+video+reasoning+from+perception+to+cognition%2E+In+Proceedings+of+the+Inter)
8. Hao Fei, Shengqiong Wu, Yafeng Ren, and Meishan Zhang. 2022. Matching structure for dual learning. In Proceedings of the International Conference on Machine Learning, ICML, pages 6373–6391. [find](https://scholar.google.com/scholar?q=Hao+Fei%2C+Shengqiong+Wu%2C+Yafeng+Ren%2C+and+Meishan+Zhang%2E+2022%2E+Matching+structure+for+dual+learning%2E+In+Proceedings+of+the+International+Conference+on+Machine+Learning%2C+ICML%2C+pages+6373%E2%80%936391%2E)
9. Vedant Gaur and Nikunj Saunshi. 2023. Reasoning in large language models through symbolic math word problems. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 5889–5903. [find](https://scholar.google.com/scholar?q=Vedant+Gaur+and+Nikunj+Saunshi%2E+2023%2E+Reasoning+in+large+language+models+through+symbolic+math+word+problems%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pag)
10. Olga Gerasimova, Nikita Severin, and Ilya Makarov. 2023. Comparative analysis of logic reasoning and graph neural networks for ontology-mediated query answering with a covering axiom. IEEE Access, 11:88074–88086. [find](https://scholar.google.com/scholar?q=Olga+Gerasimova%2C+Nikita+Severin%2C+and+Ilya+Makarov%2E+2023%2E+Comparative+analysis+of+logic+reasoning+and+graph+neural+networks+for+ontology%2Dmediated+query+answering+with+a+covering+axiom%2E+IEEE+Access%2C+11%3A)
11. Christopher Hahn, Frederik Schmitt, Julia J. Tillman, Niklas Metzger, Julian Siber, and Bernd Finkbeiner. 2022. Formal specifications from natural language. CoRR, abs/2206.01962. — [arXiv:2206.01962](https://arxiv.org/abs/2206.01962) [find](https://scholar.google.com/scholar?q=Christopher+Hahn%2C+Frederik+Schmitt%2C+Julia+J%2E+Tillman%2C+Niklas+Metzger%2C+Julian+Siber%2C+and+Bernd+Finkbeiner%2E+2022%2E+Formal+specifications+from+natural+language%2E+CoRR%2C+abs%2F2206%2E01962%2E+%E2%80%94+%5BarXiv%3A2206%2E01962%5D%28)
12. Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, David Peng, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Shafiq R. Joty, Alexander R. Fabbri,… [find](https://scholar.google.com/scholar?q=Simeng+Han%2C+Hailey+Schoelkopf%2C+Yilun+Zhao%2C+Zhenting+Qi%2C+Martin+Riddell%2C+Luke+Benson%2C+Lucy+Sun%2C+Ekaterina+Zubova%2C+Yujie+Qiao%2C+Matthew+Burtell%2C+David+Peng%2C+Jonathan+Fan%2C+Yixin+Liu%2C+Brian+Wong%2C+Malcolm+S) [our card](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
13. Yi Hu, Haotong Yang, Zhouchen Lin, and Muhan Zhang. 2023. Code prompting: a neural symbolic method for complex reasoning in large language models. CoRR, abs/2305.18507. — [arXiv:2305.18507](https://arxiv.org/abs/2305.18507) [find](https://scholar.google.com/scholar?q=Yi+Hu%2C+Haotong+Yang%2C+Zhouchen+Lin%2C+and+Muhan+Zhang%2E+2023%2E+Code+prompting%3A+a+neural+symbolic+method+for+complex+reasoning+in+large+language+models%2E+CoRR%2C+abs%2F2305%2E18507%2E+%E2%80%94+%5BarXiv%3A2305%2E18507%5D%28https%3A%2F%2Far)
14. Jie Huang and Kevin Chen-Chuan Chang. 2023. Towards reasoning in large language models: A survey. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 1049– 1065. [find](https://scholar.google.com/scholar?q=Jie+Huang+and+Kevin+Chen%2DChuan+Chang%2E+2023%2E+Towards+reasoning+in+large+language+models%3A+A+survey%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+pages+1049%E2%80%93+106) [our card](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
15. Shima Imani, Liang Du, and Harsh Shrivastava. 2023. MathPrompter: Mathematical reasoning using large language models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 37–42. [find](https://scholar.google.com/scholar?q=Shima+Imani%2C+Liang+Du%2C+and+Harsh+Shrivastava%2E+2023%2E+MathPrompter%3A+Mathematical+reasoning+using+large+language+models%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Computational+Linguist) [our card](https://md.research.elamur.ai/free/en/papers/imani-2023-mathprompter-mathematical-reasoning-large-language/)
16. Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, and Sadao Kurohashi. 2023. MultiTool-CoT: GPT-3 can use multiple external tools with chain of thought prompting. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 1522–1532. [find](https://scholar.google.com/scholar?q=Tatsuro+Inaba%2C+Hirokazu+Kiyomaru%2C+Fei+Cheng%2C+and+Sadao+Kurohashi%2E+2023%2E+MultiTool%2DCoT%3A+GPT%2D3+can+use+multiple+external+tools+with+chain+of+thought+prompting%2E+In+Proceedings+of+the+Annual+Meeting+of+th)
17. Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023. Structured chain-of-thought prompting for code generation. [find](https://scholar.google.com/scholar?q=Jia+Li%2C+Ge+Li%2C+Yongmin+Li%2C+and+Zhi+Jin%2E+2023%2E+Structured+chain%2Dof%2Dthought+prompting+for+code+generation%2E)
18. Henry Markovits and Robert Vachon. 1989. Reasoning with contrary-to-fact propositions. Journal of Experimental Child Psychology, 47:398–412. [find](https://scholar.google.com/scholar?q=Henry+Markovits+and+Robert+Vachon%2E+1989%2E+Reasoning+with+contrary%2Dto%2Dfact+propositions%2E+Journal+of+Experimental+Child+Psychology%2C+47%3A398%E2%80%93412%2E)
19. Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, and Roger Levy. 2023. LINC: A neurosymbolic approach for logical reasoning by combining language models with first-order logic provers. In Proceedings of the Conference on Empirical Methods in Natural Language… [find](https://scholar.google.com/scholar?q=Theo+Olausson%2C+Alex+Gu%2C+Ben+Lipkin%2C+Cedegao+Zhang%2C+Armando+Solar%2DLezama%2C+Joshua+Tenenbaum%2C+and+Roger+Levy%2E+2023%2E+LINC%3A+A+neurosymbolic+approach+for+logical+reasoning+by+combining+language+models+with+) [our card](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
20. OpenAI. 2023. GPT-4 technical report. abs/2303.08774. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2023%2E+GPT%2D4+technical+report%2E+abs%2F2303%2E08774%2E) [our card](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
21. CoRR, Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe.… [find](https://scholar.google.com/scholar?q=CoRR%2C+Long+Ouyang%2C+Jeffrey+Wu%2C+Xu+Jiang%2C+Diogo+Almeida%2C+Carroll+L%2E+Wainwright%2C+Pamela+Mishkin%2C+Chong+Zhang%2C+Sandhini+Agarwal%2C+Katarina+Slama%2C+Alex+Ray%2C+John+Schulman%2C+Jacob+Hilton%2C+Fraser+Kelton%2C+Luke)
22. Liangming Pan, Alon Albalak, Xinyi Wang, and William Wang. 2023. Logic-LM: Empowering large language models with symbolic solvers for faithful logical reasoning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 3806–3824. [find](https://scholar.google.com/scholar?q=Liangming+Pan%2C+Alon+Albalak%2C+Xinyi+Wang%2C+and+William+Wang%2E+2023%2E+Logic%2DLM%3A+Empowering+large+language+models+with+symbolic+solvers+for+faithful+logical+reasoning%2E+In+Proceedings+of+the+Conference+on+Em) [our card](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/)
23. Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, and Chris Callison-Burch. 2023. Bidirectional language models are also few-shot learners. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Ajay+Patel%2C+Bryan+Li%2C+Mohammad+Sadegh+Rasooli%2C+Noah+Constant%2C+Colin+Raffel%2C+and+Chris+Callison%2DBurch%2E+2023%2E+Bidirectional+language+models+are+also+few%2Dshot+learners%2E+In+Proceedings+of+the+Internationa)
24. John Alan Robinson. 1965. A machine-oriented logic based on the resolution principle. J. ACM, 12(1):23– 41. [find](https://scholar.google.com/scholar?q=John+Alan+Robinson%2E+1965%2E+A+machine%2Doriented+logic+based+on+the+resolution+principle%2E+J%2E+ACM%2C+12%281%29%3A23%E2%80%93+41%2E)
25. Abulhair Saparov and He He. 2023. Language models are greedy reasoners: A systematic formal analysis of chain-of-thought. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Abulhair+Saparov+and+He+He%2E+2023%2E+Language+models+are+greedy+reasoners%3A+A+systematic+formal+analysis+of+chain%2Dof%2Dthought%2E+In+Proceedings+of+the+International+Conference+on+Learning+Representations%2E) [our card](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/)
26. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Al… [find](https://scholar.google.com/scholar?q=Aarohi+Srivastava%2C+Abhinav+Rastogi%2C+Abhishek+Rao%2C+Abu+Awal+Md+Shoeb%2C+Abubakar+Abid%2C+Adam+Fisch%2C+Adam+R%2E+Brown%2C+Adam+Santoro%2C+Aditya+Gupta%2C+Adri%C3%A0+Garriga%2DAlonso%2C+Agnieszka+Kluska%2C+Aitor+Lewkowycz%2C+Aksh) [our card](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
27. Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, and Rui Yan. 2023. From indeterminacy to determinacy: Augmenting logical reasoning capabilities with large language models. CoRR, abs/2310.18659. — [arXiv:2310.18659](https://arxiv.org/abs/2310.18659) [find](https://scholar.google.com/scholar?q=Hongda+Sun%2C+Weikai+Xu%2C+Wei+Liu%2C+Jian+Luan%2C+Bin+Wang%2C+Shuo+Shang%2C+Ji%2DRong+Wen%2C+and+Rui+Yan%2E+2023%2E+From+indeterminacy+to+determinacy%3A+Augmenting+logical+reasoning+capabilities+with+large+language+models)
28. Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021. ProofWriter: Generating implications, proofs, and abductive statements over natural language. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 3621–3634. and Denny Zhou. 2022. Chain-of-thought prompting… [find](https://scholar.google.com/scholar?q=Oyvind+Tafjord%2C+Bhavana+Dalvi%2C+and+Peter+Clark%2E+2021%2E+ProofWriter%3A+Generating+implications%2C+proofs%2C+and+abductive+statements+over+natural+language%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Associat) [our card](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/)
29. Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2023. Next-gpt: Any-to-any multimodal llm. arXiv preprint arXiv:2309.05519. [find](https://scholar.google.com/scholar?q=Shengqiong+Wu%2C+Hao+Fei%2C+Leigang+Qu%2C+Wei+Ji%2C+and+Tat%2DSeng+Chua%2E+2023%2E+Next%2Dgpt%3A+Any%2Dto%2Dany+multimodal+llm%2E+arXiv+preprint+arXiv%3A2309%2E05519%2E)
30. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. CoRR, abs/2305.10601. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Thomas+L%2E+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+2023%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+CoRR%2C+abs%2F2305%2E1)
31. Jiacheng Ye, Chengzu Li, Lingpeng Kong, and Tao Yu. 2023. Generating data for symbolic language with large language models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 8418–8443. [find](https://scholar.google.com/scholar?q=Jiacheng+Ye%2C+Chengzu+Li%2C+Lingpeng+Kong%2C+and+Tao+Yu%2E+2023%2E+Generating+data+for+symbolic+language+with+large+language+models%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Pr)
32. Yifan Zhang, Jingqin Yang, Yang Yuan, and Andrew Chi-Chih Yao. 2023a. Cumulative reasoning with large language models. CoRR, abs/2308.04371. [find](https://scholar.google.com/scholar?q=Yifan+Zhang%2C+Jingqin+Yang%2C+Yang+Yuan%2C+and+Andrew+Chi%2DChih+Yao%2E+2023a%2E+Cumulative+reasoning+with+large+language+models%2E+CoRR%2C+abs%2F2308%2E04371%2E)
33. Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2023b. Automatic chain of thought prompting in large language models. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Zhuosheng+Zhang%2C+Aston+Zhang%2C+Mu+Li%2C+and+Alex+Smola%2E+2023b%2E+Automatic+chain+of+thought+prompting+in+large+language+models%2E+In+Proceedings+of+the+International+Conference+on+Learning+Representations%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2022-automatic-chain-thought-prompting-large/)
34. Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, and Chong Teng. 2024. Reverse multi-choice dialogue commonsense inference with graph-of-thought. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19688– 19696. [find](https://scholar.google.com/scholar?q=Li+Zheng%2C+Hao+Fei%2C+Fei+Li%2C+Bobo+Li%2C+Lizi+Liao%2C+Donghong+Ji%2C+and+Chong+Teng%2E+2024%2E+Reverse+multi%2Dchoice+dialogue+commonsense+inference+with+graph%2Dof%2Dthought%2E+In+Proceedings+of+the+AAAI+Conference+on+Ar)
35. Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. 2022. Analytical reasoning of text. In Proceedings of the North American Chapter of the Association for Computational Linguistics, pages 2306–2319. [find](https://scholar.google.com/scholar?q=Wanjun+Zhong%2C+Siyuan+Wang%2C+Duyu+Tang%2C+Zenan+Xu%2C+Daya+Guo%2C+Yining+Chen%2C+Jiahai+Wang%2C+Jian+Yin%2C+Ming+Zhou%2C+and+Nan+Duan%2E+2022%2E+Analytical+reasoning+of+text%2E+In+Proceedings+of+the+North+American+Chapter+)
36. Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. 2023a. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 2609–2634. [find](https://scholar.google.com/scholar?q=Lei+Wang%2C+Wanyu+Xu%2C+Yihuai+Lan%2C+Zhiqiang+Hu%2C+Yunshi+Lan%2C+Roy+Ka%2DWei+Lee%2C+and+Ee%2DPeng+Lim%2E+2023a%2E+Plan%2Dand%2Dsolve+prompting%3A+Improving+zero%2Dshot+chain%2Dof%2Dthought+reasoning+by+large+language+models%2E+In+P)
37. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b. Self-consistency improves chain of thought reasoning in language models. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xuezhi+Wang%2C+Jason+Wei%2C+Dale+Schuurmans%2C+Quoc+V%2E+Le%2C+Ed+H%2E+Chi%2C+Sharan+Narang%2C+Aakanksha+Chowdhery%2C+and+Denny+Zhou%2E+2023b%2E+Self%2Dconsistency+improves+chain+of+thought+reasoning+in+language+models%2E+In+P) [our card](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
38. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Xuezhi+Wang%2C+Dale+Schuurmans%2C+Maarten+Bosma%2C+Brian+Ichter%2C+Fei+Xia%2C+Ed+H%2E+Chi%2C+Quoc+V%2E+Le%2C)