Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing approaches to improve prediction accuracy and quality typically involve LLM self-improvement / self-reflection that incorporate feedback from models themselves. Despite their effectiveness, these methods are hindered by their high computational cost and lack of scalability. In this work, we propose CERET, a method for refining text generations by considering semantic stability, entailment and inter-sample uncertainty measures. Experimental results show that CERET outperforms Self-consistency and Self-rerank baselines consistently under various task setups, by ~1.6% in Rouge-1 for abstractive summarization and ~3.5% in hit rate for question answering. Compared to LLM Self-rerank method, our approach only requires 9.4% of its latency and is more cost-effective.
@article{arxiv.2406.05588,
title = {CERET: Cost-Effective Extrinsic Refinement for Text Generation},
author = {Jason Cai and Hang Su and Monica Sunkara and Igor Shalyminov and Saab Mansour},
journal= {arXiv preprint arXiv:2406.05588},
year = {2024}
}
Comments
The source code and data samples are released at https://github.com/amazon-science/CERET-LLM-refine