CERET: Cost-Effective Extrinsic Refinement for Text Generation

• URL: http://arxiv.org/abs/2406.05588v2
• Date: Sat, 02 Nov 2024 03:18:56 GMT
• Title: CERET: Cost-Effective Extrinsic Refinement for Text Generation
• Authors: Jason Cai, Hang Su, Monica Sunkara, Igor Shalyminov, Saab Mansour,
• Abstract summary: 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.
• Score: 14.43795791836198
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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