A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences
Abstract
The reasoning abilities of Large Language Models (LLMs) are becoming a central focus of study in NLP. In this paper, we consider the case of syllogistic reasoning, an area of deductive reasoning studied extensively in logic and cognitive psychology. Previous research has shown that pre-trained LLMs exhibit reasoning biases, such as content effects, avoid answering that no conclusion follows, align with human difficulties, and struggle with multi-step reasoning. We contribute to this research line by systematically investigating the effects of chain-of-thought reasoning, in-context learning (ICL), and supervised fine-tuning (SFT) on syllogistic reasoning, considering syllogisms with conclusions that support or violate world knowledge and with multiple premises. Crucially, we go beyond the standard focus on accuracy, with an in-depth analysis of the conclusions generated by the models. Our results suggest that the behavior of pre-trained LLMs can be explained by heuristics studied in cognitive science and that both ICL and SFT improve model performance on valid inferences, although only the latter can mitigate most reasoning biases while being consistent.- Anthology ID:
- 2024.emnlp-main.769
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13882–13905
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.769
- DOI:
- 10.18653/v1/2024.emnlp-main.769
- Cite (ACL):
- Leonardo Bertolazzi, Albert Gatt, and Raffaella Bernardi. 2024. A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13882–13905, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences (Bertolazzi et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.emnlp-main.769.pdf