Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913166869397504 |
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| author | Hellwig, Nils Constantin Donhauser, Niklas Fehle, Jakob Kruschwitz, Udo Wolff, Christian |
| author_facet | Hellwig, Nils Constantin Donhauser, Niklas Fehle, Jakob Kruschwitz, Udo Wolff, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28058 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis Hellwig, Nils Constantin Donhauser, Niklas Fehle, Jakob Kruschwitz, Udo Wolff, Christian Computation and Language 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. |
| title | Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.28058 |