@inproceedings{cai-etal-2024-ceret,
title = "{CERET}: Cost-Effective Extrinsic Refinement for Text Generation",
author = "Cai, Jason and
Su, Hang and
Sunkara, Monica and
Shalyminov, Igor and
Mansour, Saab",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.409",
pages = "7370--7383",
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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<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.</abstract>
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%0 Conference Proceedings
%T CERET: Cost-Effective Extrinsic Refinement for Text Generation
%A Cai, Jason
%A Su, Hang
%A Sunkara, Monica
%A Shalyminov, Igor
%A Mansour, Saab
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F cai-etal-2024-ceret
%X 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.
%U https://aclanthology.org/2024.naacl-long.409
%P 7370-7383
Markdown (Informal)
[CERET: Cost-Effective Extrinsic Refinement for Text Generation](https://aclanthology.org/2024.naacl-long.409) (Cai et al., NAACL 2024)
ACL
- Jason Cai, Hang Su, Monica Sunkara, Igor Shalyminov, and Saab Mansour. 2024. CERET: Cost-Effective Extrinsic Refinement for Text Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7370–7383, Mexico City, Mexico. Association for Computational Linguistics.