# MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction

arXiv: https://arxiv.org/abs/2305.12627 · HTML: https://mlsanity.com/paper/2305.12627/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 66 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Assessing Social and Intersectional Biases in Contextualized Word Representations](https://mlsanity.com/paper/1911.01485/) (2019)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Rationale-Augmented Ensembles in Language Models](https://mlsanity.com/paper/2207.00747/) (2022)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation](https://mlsanity.com/paper/2210.10291/) (2022)
- [Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification](https://mlsanity.com/paper/1909.06276/) (2019)
- [Solving Aspect Category Sentiment Analysis as a Text Generation Task](https://mlsanity.com/paper/2110.07310/) (2021)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction](https://mlsanity.com/paper/1906.01794/) (2019)
- [Towards Navigation by Reasoning over Spatial Configurations](https://mlsanity.com/paper/2105.06839/) (2021)
- [UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning](https://mlsanity.com/paper/2211.10986/) (2022)
- [A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification](https://mlsanity.com/paper/1807.04990/) (2018)
- [SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2302.13032/) (2023)

## Built on by (most cited first, 30 of 50)

- [Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy](https://mlsanity.com/paper/2403.00354/) (2024)
- [RVISA: Reasoning and Verification for Implicit Sentiment Analysis](https://mlsanity.com/paper/2407.02340/) (2024)
- [Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2502.13044/) (2025)
- [EFSA: Towards Event-Level Financial Sentiment Analysis](https://mlsanity.com/paper/2404.08681/) (2024)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning](https://mlsanity.com/paper/2506.09473/) (2025)
- [Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis](https://mlsanity.com/paper/2403.10214/) (2024)
- [BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.07365/) (2024)
- [Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.18078/) (2024)
- [Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.08125/) (2025)
- [LLaMA-Based Models for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2508.08649/) (2025)
- [LACA: Improving Cross-lingual Aspect-Based Sentiment Analysis with LLM Data Augmentation](https://mlsanity.com/paper/2508.09515/) (2025)
- [Advancing Cross-lingual Aspect-Based Sentiment Analysis with LLMs and Constrained Decoding for Sequence-to-Sequence Models](https://mlsanity.com/paper/2508.10366/) (2025)
- [Table-Filling via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2407.21052/) (2024)
- [Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2410.02297/) (2024)
- [PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2412.00763/) (2024)
- [STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2501.16093/) (2025)
- [Towards Semantic Integration of Opinions: Unified Opinion Concepts Ontology and Extraction Task](https://mlsanity.com/paper/2505.18703/) (2025)
- [When LLMs Team Up: The Emergence of Collaborative Affective Computing](https://mlsanity.com/paper/2506.01698/) (2025)
- [Adaptive Data Augmentation for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2401.06394/) (2024)
- [E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2405.06454/) (2024)
- [Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2406.11130/) (2024)
- [Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation](https://mlsanity.com/paper/2410.09807/) (2024)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding](https://mlsanity.com/paper/2508.10369/) (2025)
- [Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Models](https://mlsanity.com/paper/2509.02363/) (2025)
- [Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2511.23184/) (2025)
- [Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2603.13777/) (2026)
- [Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/2605.20998/) (2026)
- [How to Ask the AI: A User Perspective Survey for Large Language Model Prompting](https://mlsanity.com/paper/2608.07494/) (2026)

## Abstract (arXiv)

Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity of language expression on the results. In this work, we propose Multi-view Prompting (MvP) that aggregates sentiment elements generated in different orders, leveraging the intuition of human-like problem-solving processes from different views. Specifically, MvP introduces element order prompts to guide the language model to generate multiple sentiment tuples, each with a different element order, and then selects the most reasonable tuples by voting. MvP can naturally model multi-view and multi-task as permutations and combinations of elements, respectively, outperforming previous task-specific designed methods on multiple ABSA tasks with a single model. Extensive experiments show that MvP significantly advances the state-of-the-art performance on 10 datasets of 4 benchmark tasks, and performs quite effectively in low-resource settings. Detailed evaluation verified the effectiveness, flexibility, and cross-task transferability of MvP.