Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Mengyu Ye, Tatsuki Kuribayashi, Jun Suzuki, Goro Kobayashi, Hiroaki Funayama
Abstract
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style reasoning robustly is of interest from a probing perspective. In this study, we inspect the step-by-step reasoning ability of LLMs with a focus on negation, which is a core linguistic phenomenon that is difficult to process. In particular, we introduce several controlled settings (e.g., reasoning in case of fictional entities) to evaluate the logical reasoning abilities of the models. We observed that dozens of modern LLMs were not robust against lexical negation (e.g., plausible→implausible) when performing CoT-style reasoning, and the results highlight unique limitations in each LLM family.- Anthology ID:
- 2023.emnlp-main.912
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14753–14773
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.912
- DOI:
- 10.18653/v1/2023.emnlp-main.912
- Cite (ACL):
- Mengyu Ye, Tatsuki Kuribayashi, Jun Suzuki, Goro Kobayashi, and Hiroaki Funayama. 2023. Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14753–14773, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism (Ye et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.912.pdf