Evolutionary Prompt Design for LLM-Based Post-ASR Error Correction
This work addresses the challenge of improving ASR accuracy through better prompt design for LLMs, but it is incremental as it builds on existing in-context learning methods.
The paper tackles the problem of optimizing prompts for large language models (LLMs) used in post-automatic speech recognition (ASR) error correction, proposing an evolutionary algorithm to refine prompts and showing effectiveness on the CHiME-4 dataset from the SLT 2024 GenSEC challenge.
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.