Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models (2310.04027v2)

Abstract: 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, 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.

Enhancing Financial Sentiment Analysis via Retrieval Augmented LLMs

This paper introduces a sophisticated framework for financial sentiment analysis using retrieval-augmented LLMs. The authors address the challenges posed by traditional NLP models' limitations in parameter size and training data corpus by leveraging LLMs' capabilities. They also tackle the issues arising from the direct application of LLMs to financial sentiment analysis: the misalignment with sentiment prediction goals and the lack of contextual depth in financial news content.

Key Contributions

The paper's principal contribution is a novel framework that improves sentiment analysis precision in financial domains through two core components: instruction-tuned LLMs and retrieval augmentation. The instruction-tuned LLMs are fine-tuned to produce sentiment predictions that align closely with financial context, utilizing handcrafted instruction-following datasets specifically for sentiment analysis. The retrieval-augmentation module enhances prediction accuracy by sourcing relevant contextual data from reliable financial information outlets, thereby enriching the input provided to LLMs.

Methodology

The authors' approach begins with transforming existing sentiment datasets into instruction-following formats, which involve crafting datasets that align LLM outputs with expected sentiment labels. The proposed instruction tuning refines LLMs' generative ability to adhere strictly to sentiment prediction tasks.

The retrieval-augmenting module efficiently extracts additional context from validated financial sources through a multi-step, similarity-based retrieval process. This incorporation of external knowledge fills the contextual void typical in terse financial texts, thereby improving the model's predictive performance.

Results

Comparative evaluations demonstrate that the authors' framework substantially outperforms existing models, including specialized financial models like FinBERT and general LLMs such as ChatGPT and LLaMA, with a notable 15% to 48% improvement in accuracy and F1 scores. The integration of retrieval-augmented context results in a marked enhancement in understanding brief and context-deficient financial texts.

Implications and Future Directions

This research has significant implications for the future of financial sentiment analysis. By demonstrating the effectiveness of retrieval-augmented LLMs, it offers a compelling approach to surmounting the data sparsity issue inherent in financial texts. The framework's adaptability suggests its potential utility across different NLP tasks demanding high context sensitivity.

However, the paper also identifies a limitation in relying solely on textual similarity for retrieval, missing broader macroeconomic and microeconomic contexts. Future research could integrate these dimensions to provide a more comprehensive understanding, enhancing the efficacy of sentiment predictions in various financial scenarios. This integration could pave the way for more informed decision-making processes within financial institutions, offering a richer predictive toolset.

The proposed methodologies and their successful implementation underscore the potential to refine LLMs further, guiding future efforts in AI-driven financial analysis. The paper thus contributes to advancing the theoretical understanding and practical application of LLMs in finance, with prospects for broader AI developments in context-aware computations.