Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation. (arXiv:2209.11000v1 [cs.CL])

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 …

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