Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Hélène Sauzéon, Pierre-Yves Oudeyer
Abstract
Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, partly due to the inaccessibility of LLMs, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches, namely round-trip and prompt-based score, to selecting high-quality questions from a set of LLM-generated candidates. Our method works without the need to modify the underlying model, nor does it rely on human-annotated references — both of which are realistic constraints for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.- Anthology ID:
- 2023.findings-acl.820
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12952–12965
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.820
- DOI:
- Cite (ACL):
- Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Hélène Sauzéon, and Pierre-Yves Oudeyer. 2023. Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12952–12965, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation (Yuan et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.findings-acl.820.pdf