Selecting Better Samples From Pre-trained Llms: A Case Study On Question Generation
Yuan Xingdi, Wang Tong, Wang Yen-hsiang, Fine Emery, Abdelghani Rania, Lucas Pauline, Sauzéon Hélène, Oudeyer Pierre-yves. Arxiv 2022
[Paper]
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.
Similar Work