Abstract
While Pre-trained Language Models (PLMs) internalize a great amount of world knowledge, they have been shown incapable of recalling these knowledge to solve tasks requiring complex & multi-step reasoning. Similar to how humans develop a “chain of thought” for these tasks, how can we equip PLMs with such abilities? In this work, we explore an iterative prompting framework, a new prompting paradigm which progressively elicits relevant knowledge from PLMs for multi-step inference. We identify key limitations of existing prompting methods, namely they are either restricted to queries with a single identifiable relation/predicate, or being agnostic to input contexts, which makes it difficult to capture variabilities across different inference steps. We propose an iterative context-aware prompter, which addresses these limitations by learning to dynamically synthesize prompts conditioned on the current step’s contexts. Experiments on three datasets involving multi-step reasoning show the effectiveness of the iterative scheme and the context-aware prompter design.- Anthology ID:
- 2022.emnlp-main.174
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2714–2730
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.174
- DOI:
- 10.18653/v1/2022.emnlp-main.174
- Cite (ACL):
- Boshi Wang, Xiang Deng, and Huan Sun. 2022. Iteratively Prompt Pre-trained Language Models for Chain of Thought. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2714–2730, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Iteratively Prompt Pre-trained Language Models for Chain of Thought (Wang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.emnlp-main.174.pdf