Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers

AbstractFinancial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity of high-quality datasets. To address these issues, we leverage recent advancements in large language models (LLMs) and propose to adapt proxy tuning for financial sentiment analysis. Proxy tuning efficiently transfers knowledge from a pre-trained expert model to a controllable base model by incorporating logit differences, steering the base model toward the desired sentiment representation. Our method offers significant advantages: (1) it is training-free, reducing computational demands and data dependency; (2) it achieves promising performance, with a 36.67% improvement over the base model and over 90% of the tuned model’s performance; and (3) it is highly adaptable, functioning in a plug-and-play manner without requiring access to model architectures or weights. These results demonstrate the potential of proxy tuning as an efficient and practical solution for financial sentiment analysis in data-scarce scenarios.