Large Language Model Adaptation for Financial Sentiment Analysis

Understand

Natural language processing (NLP) has recently gained relevance within financial institutions by providing highly valuable insights into companies and markets' financial documents.

• However, the landscape of the financial domain presents extra challenges for NLP, due to the complexity of the texts and the use of specific terminology.
• Generalist language models tend to fall short in tasks specifically tailored for finance, even when using large language models (LLMs) with great natural language understanding and generative capabilities.
• This paper presents a study on LLM adaptation methods targeted at the financial domain and with high emphasis on financial sentiment analysis.