Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers
The paper proposes proxy tuning with large language models to enhance financial sentiment analysis, addressing data scarcity and computation barriers.
What it examines
This study addresses the challenge of Financial Sentiment Analysis by leveraging large language models and proxy tuning. It transfers knowledge from a fine-tuned expert model to a base model without extensive training. The approach handles complex sentiment expressions and data scarcity, offering a resource-efficient solution for financial insights.
What it concludes
The study shows a resource-efficient method that nearly matches fine-tuned performance using proxy tuning for financial sentiment analysis. This approach can support algorithmic trading, risk assessment, and market monitoring while reducing training costs. It paves the way for broader applications in finance and encourages further research on proxy-based model tuning.
Evidence objects
Wang and Bao unveil a proxy tuning method for financial sentiment analysis, solving scarcity and cost issues by adjusting a base model with logit differences from fine-tuned model via LoRA.
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Boosting accuracy by $36.67%$ and reducing gap by $93%$, the proxy tuning method shows robust performance on benchmarks including Financial Phrasebank, FiQA-SA, Twitter Financial Sentiment, and News With GPT Instructions.
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Beyond impressive accuracy, the study reveals proxy tuning captures subtle financial sentiment, offering practical advantages for resource-limited institutions while highlighting potential challenges in dynamic real-world environments and long-term model stability.
key_findings bullet 3 · key_findings · validation V0
A novel study tackles key challenges in financial sentiment analysis using training-free proxy tuning. Overcoming data scarcity and high computational cost via a plug-and-play method, this work reveals significant performance gains for financial large language models. Its unique approach offers a truly compelling perspective that distinguishes it from conventional techniques.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … its of adapting proxy tuning for financial sentiment analysis as follows: (1) Training-free: This approach significantly reduces computational resource requirements, while …
Source row: 1617 · abstract type: snippet