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    <abstract>In-context learning (ICL) relies heavily on high-quality demonstrations drawn from large annotated corpora. Existing approaches detect noisy annotations by ranking local perplexities, presuming that noisy samples yield higher perplexities than their clean counterparts. However, this assumption breaks down when the noise ratio is high and many demonstrations are flawed.We re-examine the perplexity-based paradigm for text generation under noisy annotations, highlighting two sources of bias in perplexity: the annotation itself and the domain-specific knowledge inherent in large language models (LLMs). To overcome these biases, we introduce a dual-debiasing framework that uses synthesized neighbors to explicitly correct perplexity estimates, yielding a robust Sample Cleanliness Score. This metric uncovers absolute sample cleanliness regardless of the overall corpus noise level.Extensive experiments demonstrate our method’s superior noise-detection capabilities and show that its final ICL performance is comparable to that of a fully clean demonstration corpus. Moreover, our approach remains robust even when noise ratios are extremely high.</abstract>
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%0 Conference Proceedings
%T Dual Debiasing for Noisy In-Context Learning for Text Generation
%A Liang, Siqi
%A Ahn, Sumyeong
%A Dhillon, Paramveer
%A Zhou, Jiayu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liang-etal-2025-dual
%X In-context learning (ICL) relies heavily on high-quality demonstrations drawn from large annotated corpora. Existing approaches detect noisy annotations by ranking local perplexities, presuming that noisy samples yield higher perplexities than their clean counterparts. However, this assumption breaks down when the noise ratio is high and many demonstrations are flawed.We re-examine the perplexity-based paradigm for text generation under noisy annotations, highlighting two sources of bias in perplexity: the annotation itself and the domain-specific knowledge inherent in large language models (LLMs). To overcome these biases, we introduce a dual-debiasing framework that uses synthesized neighbors to explicitly correct perplexity estimates, yielding a robust Sample Cleanliness Score. This metric uncovers absolute sample cleanliness regardless of the overall corpus noise level.Extensive experiments demonstrate our method’s superior noise-detection capabilities and show that its final ICL performance is comparable to that of a fully clean demonstration corpus. Moreover, our approach remains robust even when noise ratios are extremely high.
%R 10.18653/v1/2025.findings-acl.665
%U https://aclanthology.org/2025.findings-acl.665/
%U https://doi.org/10.18653/v1/2025.findings-acl.665
%P 12855-12868

Markdown (Informal)

[Dual Debiasing for Noisy In-Context Learning for Text Generation](https://aclanthology.org/2025.findings-acl.665/) (Liang et al., Findings 2025)

• Dual Debiasing for Noisy In-Context Learning for Text Generation (Liang et al., Findings 2025)

ACL

• Siqi Liang, Sumyeong Ahn, Paramveer Dhillon, and Jiayu Zhou. 2025. Dual Debiasing for Noisy In-Context Learning for Text Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12855–12868, Vienna, Austria. Association for Computational Linguistics.