Abstract
Compositional reasoning across texts has been a long-standing challenge in natural language processing. With large language models like GPT-4 taking over the field, prompting techniques such as chain-of-thought (CoT) were proposed to unlock compositional, multi-step reasoning capabilities of LLMs. Despite their success, the prompts demand significant human effort to discover and validate them. Our work draws attention to the idea of transferring task-specific inductive biases from finetuned models to prompts, as a way of improving GPT-4’s compositional reasoning capabilities. To leverage these inductive biases, we formulate prompt templates to ease the transfer of inductive biases. The experimental results on multi-hop question answering and numerical reasoning over text show that our proposed prompt scheme shows competitive zero-shot and few-shot performances compared to existing prompts on complicated reasoning tasks, highlighting the importance of adopting the validated biases of the previous paradigm.- Anthology ID:
- 2023.findings-emnlp.245
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3763–3775
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.245
- DOI:
- 10.18653/v1/2023.findings-emnlp.245
- Cite (ACL):
- Jeonghwan Kim, Giwon Hong, Sung-Hyon Myaeng, and Joyce Whang. 2023. FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3763–3775, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4 (Kim et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.findings-emnlp.245.pdf