mlsanityData to 7 Oct 2026

Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy

arXiv 2403.00354 ↗First posted 1 Mar 2024cs.CLcited by 12

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Abstract (arXiv)

In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promising results. However, they still suffer from imprecise predictions and limited interpretability, caused by data scarcity and inadequate modeling of the quadruplet composition process. In this paper, we propose Self-Consistent Reasoning-based Aspect-sentiment quadruple Prediction (SCRAP), optimizing its model to generate reasonings and the corresponding sentiment quadruplets in sequence. SCRAP adopts the Extract-Then-Assign reasoning strategy, which closely mimics human cognition. In the end, SCRAP significantly improves the model's ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in ASQP.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

12 of 26 references.

References · 12

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  6. 2022Large Language Models Are Reasoning Teacherscited by 318
  7. 2022Teaching Small Language Models to Reasoncited by 279
  8. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Stepcited by 144
  9. 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 134
  10. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 99
  11. 2023MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Predictioncited by 49
  12. 2022Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentationcited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 3.

12 papers.

  1. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 24
  2. 2024MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysiscited by 10
  3. 2025Can Large Language Models be Effective Online Opinion Miners?cited by 3
  4. 2025Personalized Reward Modeling for Text-to-Image Generationcited by 3
  5. 2024Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysiscited by 2
  6. 2025STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Predictioncited by 2
  7. 2025RPM: Reasoning-Level Personalization for Black-Box Large Language Modelscited by 2
  8. 2025When LLMs Team Up: The Emergence of Collaborative Affective Computingcited by 2
  9. 2024Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysiscited by 1
  10. 2026Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Predictioncited by 0
  11. 2026DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysiscited by 0
  12. 2026LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Datasetcited by 0