@inproceedings{kirtania-etal-2024-logic,
title = "{LOGIC}-{LM}++: Multi-Step Refinement for Symbolic Formulations",
author = "Kirtania, Shashank and
Gupta, Priyanshu and
Radhakrishna, Arjun",
editor = "Dalvi Mishra, Bhavana and
Durrett, Greg and
Jansen, Peter and
Lipkin, Ben and
Neves Ribeiro, Danilo and
Wong, Lionel and
Ye, Xi and
Zhao, Wenting",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlrse-1.6",
pages = "56--63",
abstract = "In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. While current approaches leverage formal languages as intermediate representation for these reasoning problems, they still struggle with generating intermediate for-mal specifications with great correctness and in refining these representations. To address these issues, this paper proposes Logic-LM++, an improvement on Logic-LM (Pan et al., 2023). It uses the ability of LLMs to do pairwise comparisons, allowing the evaluation of the refinements suggested by the LLM. The paper demonstrates that Logic-LM++ outperforms Logic-LM and LLM based techniques on natural language reasoning tasks on two datasets, FOLIO, ProofWriter and AR-LSAT. Logic-LM++ show an average improvement of 18.5{\%} on standard prompting, 12.3{\%} on chain of thought prompting and 5{\%} on Logic-LM.",
}
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<abstract>In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. While current approaches leverage formal languages as intermediate representation for these reasoning problems, they still struggle with generating intermediate for-mal specifications with great correctness and in refining these representations. To address these issues, this paper proposes Logic-LM++, an improvement on Logic-LM (Pan et al., 2023). It uses the ability of LLMs to do pairwise comparisons, allowing the evaluation of the refinements suggested by the LLM. The paper demonstrates that Logic-LM++ outperforms Logic-LM and LLM based techniques on natural language reasoning tasks on two datasets, FOLIO, ProofWriter and AR-LSAT. Logic-LM++ show an average improvement of 18.5% on standard prompting, 12.3% on chain of thought prompting and 5% on Logic-LM.</abstract>
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%0 Conference Proceedings
%T LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations
%A Kirtania, Shashank
%A Gupta, Priyanshu
%A Radhakrishna, Arjun
%Y Dalvi Mishra, Bhavana
%Y Durrett, Greg
%Y Jansen, Peter
%Y Lipkin, Ben
%Y Neves Ribeiro, Danilo
%Y Wong, Lionel
%Y Ye, Xi
%Y Zhao, Wenting
%S Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kirtania-etal-2024-logic
%X In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. While current approaches leverage formal languages as intermediate representation for these reasoning problems, they still struggle with generating intermediate for-mal specifications with great correctness and in refining these representations. To address these issues, this paper proposes Logic-LM++, an improvement on Logic-LM (Pan et al., 2023). It uses the ability of LLMs to do pairwise comparisons, allowing the evaluation of the refinements suggested by the LLM. The paper demonstrates that Logic-LM++ outperforms Logic-LM and LLM based techniques on natural language reasoning tasks on two datasets, FOLIO, ProofWriter and AR-LSAT. Logic-LM++ show an average improvement of 18.5% on standard prompting, 12.3% on chain of thought prompting and 5% on Logic-LM.
%U https://aclanthology.org/2024.nlrse-1.6
%P 56-63
Markdown (Informal)
[LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations](https://aclanthology.org/2024.nlrse-1.6) (Kirtania et al., NLRSE-WS 2024)
ACL
- Shashank Kirtania, Priyanshu Gupta, and Arjun Radhakrishna. 2024. LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations. In Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024), pages 56–63, Bangkok, Thailand. Association for Computational Linguistics.