Large Language Models for Czech Aspect-Based Sentiment Analysis

Jakub Šmíd

doi:10.1007/978-3-032-02551-7_3

2026-01-01

PRE

Jakub Šmíd *

Pavel Přibáň

Kral, Pavel

DOI：10.1007/978-3-032-02551-7_3

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基于方面的情感分析（ABSA）是一种细粒度的情感分析任务，旨在识别实体特定方面的情感。尽管大型语言模型（LLMs）在多种自然语言处理（NLP）任务中表现出色，但它们在捷克语ABSA方面的能力仍 largely unexplored。本研究对19个不同规模和架构的LLMs进行了全面的捷克语ABSA评估，比较了它们在零样本、少样本和微调场景下的表现。结果表明，针对ABSA微调的小型领域特定模型在零样本和少样本设置中优于通用LLMs，而微调后的LLMs达到了最先进的结果。我们分析了多语言性、模型规模和时效性等因素如何影响性能，并进行了错误分析，突出了关键挑战，特别是在方面术语预测方面。我们的发现为LLMs在捷克语ABSA中的适用性提供了见解，并为该领域的未来研究提供了指导。

Keyword:

Aspect-based sentiment analysis

Sentiment analysis

Large language models

Prompting

期刊

TEXT, SPEECH, AND DIALOGUE, TSD 2025, PT II

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university of west bohemia pilsen

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1.6K

论文数: 1.3K

被引数: 8

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