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

本文研究使用基于解码器的生成式 Transformer 来提取对俄罗斯新闻文章中的命名实体的情感分析能力，通过对 RuSentNE-2023 数据集进行实验，发现在无监督和有监督转换器中，零 - shot 方法的结果与以 BERT-base 为基线的有监督转换器相似，而经过 THoR 理论的 Fine-tuned Flan-T5 模型在推理能力上实现了至少 5% 的增长，并以 Fine-tuned Flan-T5-xl 取得了在 RuSentNE-2023 情感分析中超过先前基于 Transformer 的分类器的最佳结果。