Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
This work addresses financial sentiment analysis for valuation and investment decision-making, representing an incremental improvement by combining existing techniques like retrieval-augmentation and instruction-tuning.
The paper tackled the problem of financial sentiment analysis by introducing a retrieval-augmented LLMs framework to address challenges like pre-training discrepancies and lack of context in financial news, achieving performance gains of 15% to 48% in accuracy and F1 score compared to traditional models and LLMs.
Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs and predicting the sentiment label can compromise their predictive performance. Furthermore, the succinct nature of financial news, often devoid of sufficient context, can significantly diminish the reliability of LLMs' sentiment analysis. To address these challenges, we introduce a retrieval-augmented LLMs framework for financial sentiment analysis. This framework includes an instruction-tuned LLMs module, which ensures LLMs behave as predictors of sentiment labels, and a retrieval-augmentation module which retrieves additional context from reliable external sources. Benchmarked against traditional models and LLMs like ChatGPT and LLaMA, our approach achieves 15\% to 48\% performance gain in accuracy and F1 score.