# Pre-trained Large Language Models for Financial Sentiment Analysis

arXiv: https://arxiv.org/abs/2401.05215 · HTML: https://mlsanity.com/paper/2401.05215/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning](https://mlsanity.com/paper/2307.10234/) (2023)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [Sentiment Analysis of Financial News Articles using Performance Indicators](https://mlsanity.com/paper/1811.11008/) (2018)
- [Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning](https://mlsanity.com/paper/1901.00400/) (2018)

## Built on by (most cited first, 5 of 5)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Understanding and Enforcing Weight Disentanglement in Task Arithmetic](https://mlsanity.com/paper/2604.17078/) (2026)
- [Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis](https://mlsanity.com/paper/2507.22936/) (2025)
- [Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading](https://mlsanity.com/paper/2508.07408/) (2025)
- [Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques](https://mlsanity.com/paper/2601.08302/) (2026)

## Abstract (arXiv)

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.