Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation

• URL: http://arxiv.org/abs/2209.11000v1
• Date: Thu, 22 Sep 2022 13:33:48 GMT
• Title: Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
• Authors: Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Pauline Lucas, H\'el\`ene Sauz\'eon and Pierre-Yves Oudeyer
• Abstract summary: Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. We propose two prompt-based approaches to selecting high-quality questions from a set of LLM-generated candidates. Our method works under the constraints of 1) a black-box (non-modifiable) question generation model and 2) lack of access to human-annotated references.
• Score: 22.294762359009052
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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 to selecting high-quality questions from a set of LLM-generated candidates. Our method works under the constraints of 1) a black-box (non-modifiable) question generation model and 2) lack of access to human-annotated references -- both of which are realistic limitations 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.

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