A Survey of Zero-Shot and Few-Shot Learning with Large Language Models for Financial Sentiment Analysis
TL;DR
Overall, LLMs offer a flexible and annotation-light alternative to traditional fine-tuning, but their successful deployment in finance will depend on robust prompt engineering and solid factual grounding.
Abstract
Financial sentiment analysis has long relied on labeled data to fine-tune models like FinBERT, a process that is both costly and time-consuming. The arrival of large language models (LLMs) has changed the landscape: with zero-shot and few-shot prompting, one can now extract sentiment from financial texts using few or no annotated examples. This survey takes stock of how LLMs are being applied to this task. It begins by clarifying the core ideas behind in-context learning and chain-of-thought prompting. It then examines a range of prompt designs that have been developed to cope with the peculiarities of financial writing, such as numerical expressions, implicit sentiment and long documents. A comparison of LLM performance on standard benchmarks against fine-tuned domain models shows that general-purpose LLMs are often competitive, especially when prompts are carefully crafted. Yet three problems remain unresolved: numerical reasoning errors, hallucination, and the practical hurdles of cost, latency, and privacy. These challenges are discussed in detail, and retrieval-augmented generation, trustworthiness frameworks, and efficient open-source models are pointed out as the most promising paths forward. Overall, LLMs offer a flexible and annotation-light alternative to traditional fine-tuning, but their successful deployment in finance will depend on robust prompt engineering and solid factual grounding.