Building upon the strength of modern large language models (LLMs), generative error correction (GEC) has emerged as a promising paradigm that can elevate the performance of modern automatic speech recognition (ASR) systems. One representative approach is to leverage in-context learning to prompt LLMs so that a better hypothesis can be generated by the LLMs based on a carefully-designed prompt and an $N$-best list of hypotheses produced by ASR systems. However, it is yet unknown whether the existing prompts are the most effective ones for the task of post-ASR error correction. In this context, this paper first explores alternative prompts to identify an initial set of effective prompts, and then proposes to employ an evolutionary prompt optimization algorithm to refine the initial prompts. Evaluations results on the CHiME-4 subset of the Task $1$ of the SLT $2024$ GenSEC challenge show the effectiveness and potential of the proposed algorithms.

翻译：基于现代大语言模型（LLMs）的强大能力，生成式错误修正（GEC）已成为一种有前景的范式，能够提升现代自动语音识别（ASR）系统的性能。一种代表性方法是利用上下文学习来提示LLMs，使其能够基于精心设计的提示和ASR系统产生的$N$-最佳假设列表，生成更优的假设。然而，现有提示是否为语音识别后处理错误修正任务的最有效方案尚未可知。在此背景下，本文首先探索了替代提示以确定一组初始有效提示，进而提出采用进化提示优化算法对这些初始提示进行精炼。在SLT $2024$ GenSEC挑战赛任务$1$的CHiME-4子集上的评估结果表明，所提算法具有有效性和潜力。