Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
For practitioners in sentiment analysis, this provides a practical and efficient alternative to fine-tuning that requires fewer annotated examples and lower computational costs.
LLM-MvP closes the performance gap between few-shot prompting and fine-tuned models for Aspect-Based Sentiment Analysis, achieving competitive or superior results while reducing computational overhead.
Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring substantially fewer annotated examples while achieving notable improvements over zero-shot baselines. However, a performance gap remained compared to models fine-tuned on hundreds of examples, and the computational costs of LLM inference present practical barriers to deployment. We introduce LLM-based Multi-View Prompting (LLM-MvP), which adapts the multi-view principle of considering multiple element orderings to LLM prompting. By combining schema-constrained decoding with a context-free grammar and prefix batching, LLM-MvP achieves performance competitive or superior to fine-tuned approaches while substantially reducing computational overhead. Extensive experiments across five benchmark datasets demonstrate that LLM-MvP closes the gap between few-shot prompting and fine-tuned models, offering a practical and efficient solution for ABSA.