Pre-trained Large Language Models for Financial Sentiment Analysis
This work addresses sentiment classification for financial news titles, an incremental improvement using existing methods on domain-specific data.
The paper tackled the problem of financial sentiment analysis on news titles, which is challenging due to limited training data, by adapting the pretrained Llama2-7B model with supervised fine-tuning, resulting in significant outperformance over previous state-of-the-art algorithms.
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.