评测19个大模型在捷克语细粒度情感分析中的表现
Large Language Models for Czech Aspect-Based Sentiment Analysis
- 对比不同规模和架构的19个大模型在零样本、少样本和微调下的表现
- 小众领域微调模型在零/少样本下优于通用大模型，微调后达顶尖水平
- 揭示多语言性、模型大小和时效性对性能的影响，适合本地化NLP研究者
方面级情感分析（ABSA）是一项细粒度自然语言处理任务，旨在识别对实体特定方面的观点极性。尽管大语言模型（LLMs）在多种NLP任务中表现优异，其在捷克语ABSA中的潜力仍不明确。本文系统评估了19种不同规模与架构的LLMs在捷克语ABSA上的表现，涵盖零样本、少样本及微调场景。结果表明，小规模领域专用模型在零样本与少样本设置中优于通用大模型；而经过微调的大模型则达到当前最优性能。我们分析了多语言性、模型大小及发布时间等因素对性能的影响，并进行了错误分析，指出方面词预测仍是主要挑战。研究为捷克语ABSA中大模型的应用提供了洞见，并为后续研究提供指导。
原文摘要 · Abstract (English)
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to identify sentiment toward specific aspects of an entity. While large language models (LLMs) have shown strong performance in various natural language processing (NLP) tasks, their capabilities for Czech ABSA remain largely unexplored. In this work, we conduct a comprehensive evaluation of 19 LLMs of varying sizes and architectures on Czech ABSA, comparing their performance in zero-shot, few-shot, and fine-tuning scenarios. Our results show that small domain-specific models fine-tuned for ABSA outperform general-purpose LLMs in zero-shot and few-shot settings, while fine-tuned LLMs achieve state-of-the-art results. We analyze how factors such as multilingualism, model size, and recency influence performance and present an error analysis highlighting key challenges, particularly in aspect term prediction. Our findings provide insights into the suitability of LLMs for Czech ABSA and offer guidance for future research in this area.
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