Pre-trained Large Language Models for Financial Sentiment Analysis
Wei Luo Dihong Gong
Financial sentiment analysis refers to classifying financial text contents
into sentiment categories (e.g. positive, negative, and neutral). In this
paper, we focus on the classification of financial news title, which is a
challenging task due to a lack of large amount of training samples. To overcome
this difficulty, we propose to adapt the pretrained large language models
(LLMs) [1, 2, 3] to solve this problem. The LLMs, which are trained from huge
amount of text corpora,have an advantage in text understanding and can be
effectively adapted to domain-specific task while requiring very few amount of
training samples. In particular, we adapt the open-source Llama2-7B model
(2023) with the supervised fine-tuning (SFT) technique [4]. Experimental
evaluation shows that even with the 7B model (which is relatively small for
LLMs), our approach significantly outperforms the previous state-of-the-art
algorithms.