Parameter-Efficient Adaptation of Qwen2.5 for Aspect-Based Sentiment Analysis Using Low-Rank Adaptation and Parameter-Efficient Fine-Tuning

Aspect-based sentiment analysis (ABSA) plays a vital role in deriving fine-grained sentiment from textual content. As large language models (LLMs) are increasingly adopted for automated data annotation in natural language processing (NLP), concerns have emerged regarding the accuracy of their output...

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Autori principali:	Pei Ying Lim, Chuk Fong Ho, Chi Wee Tan
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2026-03-01
Serie:	Engineering Proceedings
Soggetti:	aspect-based sentiment analysis large language models Qwen2.5 parameter-efficient fine-tuning low-rank adaptation
Accesso online:	https://www.mdpi.com/2673-4591/128/1/15
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