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.
@article{arxiv.2605.28058,
title = {Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis},
author = {Nils Constantin Hellwig and Niklas Donhauser and Jakob Fehle and Udo Kruschwitz and Christian Wolff},
journal= {arXiv preprint arXiv:2605.28058},
year = {2026}
}