CorrectionLM: Self-Corrections with SLM for Dialogue State Tracking

Fuente: arXiv
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Auteurs principaux: Lee, Chia-Hsuan, Cheng, Hao, Ostendorf, Mari
Format: Preprint
Publié: 2024
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author Lee, Chia-Hsuan
Cheng, Hao
Ostendorf, Mari
author_facet Lee, Chia-Hsuan
Cheng, Hao
Ostendorf, Mari
contents Large language models (LLMs) have demonstrated self-improvement capabilities via feedback and refinement, but current small language models (SLMs) have had limited success in this area. Existing correction approaches often rely on distilling knowledge from LLMs, which imposes significant computation demands. In this work, we introduce CORRECTIONLM, a novel correction framework that enables SLMs to self-correct using in-context exemplars without LLM involvement. Applied to two dialogue state tracking (DST) tasks in low-resource settings, CORRECTIONLM achieves results similar to a state-of-the-art LLM at a small fraction of the computation costs.
format Preprint
id arxiv_https___arxiv_org_abs_2410_18209
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle CorrectionLM: Self-Corrections with SLM for Dialogue State Tracking
Lee, Chia-Hsuan
Cheng, Hao
Ostendorf, Mari
Computation and Language
Large language models (LLMs) have demonstrated self-improvement capabilities via feedback and refinement, but current small language models (SLMs) have had limited success in this area. Existing correction approaches often rely on distilling knowledge from LLMs, which imposes significant computation demands. In this work, we introduce CORRECTIONLM, a novel correction framework that enables SLMs to self-correct using in-context exemplars without LLM involvement. Applied to two dialogue state tracking (DST) tasks in low-resource settings, CORRECTIONLM achieves results similar to a state-of-the-art LLM at a small fraction of the computation costs.
title CorrectionLM: Self-Corrections with SLM for Dialogue State Tracking
topic Computation and Language
url https://arxiv.org/abs/2410.18209