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Dual Debiasing for Noisy In-Context Learning for Text Generation
Siqi Liang1, Sumyeong Ahn2, Paramveer S Dhillon1
1University of Michigan, Ann Arbor, MI, USA.
Findings of ACL. ACL
|February 11, 2026
Summary
This study introduces a new method to identify noisy data for in-context learning (ICL). Our approach accurately detects flawed demonstrations, even with high noise, improving ICL performance.
Area of Science:
- Artificial Intelligence
- Natural Language Processing
Background:
- In-context learning (ICL) depends on high-quality data.
- Current methods for detecting noisy data using perplexity fail when noise levels are high.
Purpose of the Study:
- To address the limitations of existing perplexity-based noise detection methods in ICL.
- To develop a robust metric for identifying sample cleanliness irrespective of corpus noise levels.
Main Methods:
- Re-examined perplexity biases from annotations and LLM domain knowledge.
- Introduced a dual-debiasing framework using synthesized neighbors to correct perplexity.
- Developed a robust Sample Cleanliness Score.
Main Results:
- The proposed Sample Cleanliness Score accurately identifies noisy samples across varying noise ratios.
- The method demonstrates superior noise-detection capabilities compared to existing approaches.
- ICL performance using the proposed method is comparable to using a fully clean dataset.
Conclusions:
- The dual-debiasing framework effectively overcomes perplexity biases in noisy ICL settings.
- The Sample Cleanliness Score provides a reliable measure of data quality.
- This approach offers robust performance even with extremely high noise ratios in demonstration data.
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