CERET: Cost-Effective Extrinsic Refinement for Text Generation
<|reference_start|>CERET: Cost-Effective Extrinsic Refinement for Text Generation: 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.<|reference_end|>
@article{cai2024ceret:,
    title={CERET: Cost-Effective Extrinsic Refinement for Text Generation},
    author={Jason Cai, Hang Su, Monica Sunkara, Igor Shalyminov, Saab Mansour},
    journal={Proceedings of the 2024 Conference of the North American Chapter
  of the Association for Computational Linguistics: Human Language Technologies
  (Volume 1: Long Papers)},
    year={2024},
    doi={10.18653/v1/2024.naacl-long.409},
    archivePrefix={arXiv},
    eprint={2406.05588},
    primaryClass={cs.CL cs.AI cs.LG}
}