Language Models of Code are Few-Shot Commonsense Learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig
Abstract
We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event or a reasoning-graph.To employ large language models (LMs) for this task, existing approaches ‘serialize’ the output graph as a flat list of nodes and edges.Although feasible, these serialized graphs strongly deviate from the natural language corpora that LMs were pre-trained on, hindering LMs from generating them correctly. In this paper, we show that when we instead frame structured commonsense reasoning tasks as code generation tasks, pre-trained LMs of code are better structured commonsense reasoners than LMs of natural language, even when the downstream task does not involve source code at all.We demonstrate our approach across three diverse structured commonsense reasoning tasks. In all these natural language tasks, we show that using our approach, a code generation LM (codex) outperforms natural-LMs that are fine-tuned on the target task (T5) and other strong LMs such as GPT-3 in the few-shot setting.- Anthology ID:
- 2022.emnlp-main.90
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1384–1403
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.90
- DOI:
- Cite (ACL):
- Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig. 2022. Language Models of Code are Few-Shot Commonsense Learners. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1384–1403, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Language Models of Code are Few-Shot Commonsense Learners (Madaan et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.90.pdf