Abstract
In this paper we investigate the use of decoder-based generative transformers for extracting sentiment towards the named entities in Russian news articles. We study sentiment analysis capabilities of instruction-tuned large language models (LLMs). We consider the dataset of RuSentNE-2023 in our study. The first group of experiments was aimed at the evaluation of zero-shot capabilities of LLMs with closed and open transparencies. The second covers the fine-tuning of Flan-T5 using the "chain-of-thought" (CoT) three-hop reasoning framework (THoR). We found that the results of the zero-shot approaches are similar to the results achieved by baseline fine-tuned encoder-based transformers (BERT-base). Reasoning capabilities of the fine-tuned Flan-T5 models with THoR achieve at least 5% increment with the base-size model compared to the results of the zero-shot experiment. The best results of sentiment analysis on RuSentNE-2023 were achieved by fine-tuned Flan-T5-xl, which surpassed the results of previous state-of-the-art transformer-based classifiers. Our CoT application framework is publicly available: this https URL
Abstract (translated)
在本文中，我们研究了使用基于解码器的生成转换器提取针对俄罗斯新闻文章中命名实体的情感。我们研究了指令微调的大型语言模型的情感分析能力。在我们的研究中，我们考虑了RuSentNE-2023数据集。第一组实验旨在评估LLMs的零样本性能。第二组实验涉及使用“思考链”（CoT）三步推理框架（THoR）对Flan-T5进行微调。我们发现，零样本方法的结果与基线微调的编码器基转换器类似。使用THoR对微调的Flan-T5模型的推理能力至少与基线大小模型相比增加了5%。在RuSentNE-2023上的情感分析最佳结果是由微调的Flan-T5-xl取得的，这超过了以往基于转换器的分类器的最佳结果。我们的CoT应用框架是公开可用的：这是https://this URL。
URL
https://arxiv.org/abs/2404.12342
