Web: http://arxiv.org/abs/2209.11000

Sept. 23, 2022, 1:15 a.m. | Xingdi Yuan, Tong Wang, Yen-Hsiang Wang, Emery Fine, Rania Abdelghani, Pauline Lucas, Hélène Sauzéon, Pierre-Yves Oudeyer

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv case case study llms study

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