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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Bibliografiske detaljer

Principais autores:	Pei Ying Lim, Chuk Fong Ho, Chi Wee Tan
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2026-03-01
Serier:	Engineering Proceedings
Fag:	aspect-based sentiment analysis large language models Qwen2.5 parameter-efficient fine-tuning low-rank adaptation
Online adgang:	https://www.mdpi.com/2673-4591/128/1/15
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