CERET: Cost-Effective Extrinsic Refinement for Text Generation
arxiv(2024)
摘要
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
Rouge-1 for abstractive summarization and 3.5
answering. Compared to LLM Self-rerank method, our approach only requires 9.4
of its latency and is more cost-effective.
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