mlsanityData to 14 Oct 2026

Improving In-Context Learning with Prediction Feedback for Sentiment Analysis

arXiv 2406.02911 ↗First posted 5 Jun 2024cs.CLcited by 4

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Abstract (arXiv)

Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sentiments still remains a challenge. Inspired by the human ability to adjust understanding via feedback, this paper enhances ICL by incorporating prior predictions and feedback, aiming to rectify sentiment misinterpretation of LLMs. Specifically, the proposed framework consists of three steps: (1) acquiring prior predictions of LLMs, (2) devising predictive feedback based on correctness, and (3) leveraging a feedback-driven prompt to refine sentiment understanding. Experimental results across nine sentiment analysis datasets demonstrate the superiority of our framework over conventional ICL methods, with an average F1 improvement of 5.95%.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

19 of 31 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
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  8. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
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  10. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  11. 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 210
  12. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
7 more
  1. 2022In-context Examples Selection for Machine Translationcited by 172
  2. 2023Unified Demonstration Retriever for In-Context Learningcited by 142
  3. 2018Deep Learning for Sentiment Analysis : A Surveycited by 139
  4. 2023Is ChatGPT a Good Sentiment Analyzer? A Preliminary Studycited by 96
  5. 2022Complementary Explanations for Effective In-Context Learningcited by 71
  6. 2020Investigating Societal Biases in a Poetry Composition Systemcited by 48
  7. 2023Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selectioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspectivecited by 6
  2. 2026Learning to Select Visual In-Context Demonstrationscited by 2
  3. 2025Targeted Distillation for Sentiment Analysiscited by 1
  4. 2024SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysiscited by 0