Exploring the Robustness of In-Context Learning with Noisy Labels
Open accessAbstract
Recently, the mysterious In-Context Learning (ICL) ability exhibited by Transformer architectures, especially in large language models (LLMs), has sparked significant research interest. However, the resilience of Transformers’ in-context learning capabilities in the presence of noisy samples, prevalent in both training corpora and prompt demonstrations, remains underexplored. In this paper, inspired by prior research that studies ICL ability using simple function classes, we take a closer look at this problem by investigating the robustness of Transformers against noisy labels. Specifically, we first conduct a thorough evaluation and analysis of the robustness of Transformers against noisy labels during in-context learning and show that they exhibit notable resilience against diverse types of noise in demonstration labels. Furthermore, we delve deeper into this problem by exploring whether introducing noise into the training set, akin to a form of data augmentation, enhances such robustness during inference, and find that such noise can indeed improve the robustness of ICL. Overall, our fruitful analysis and findings provide a comprehensive understanding of the resilience of Transformer models against label noises during ICL and provide valuable insights into the research on Transformers in natural language processing. Our code is available at https://github.com/InezYu0928/in-context-learning.
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ICA is stealthy: its prompt is in natural language form and cannot be easily detected.
“Stealthy.While adversarial suffix attacks [61,60] may be easily detected with a simple perplexity filter [1,16], the prompt of ICA is thoroughly in a natural language form and cannot be easily detected.”
From Jailbreak and Guard Aligned Language Models With Only Few In-Context Demonstrations (2026) · cites this paper as [61]
The Introduction discusses safety concerns about manipulated in-context demonstrations, while Section 3 evaluates robustness to noisy labels in a linear-regression task. Neither passage addresses an ICA prompt's natural-language form or whether it is difficult to detect; searches for those claims and for perplexity-filter detection found no supporting passage. Page numbers were unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredICA achieved 81% Attack Success Rate (ASR) on GPT-4 evaluated by the AdvBench dataset.
“ICA achieved 81% Attack Success Rate (ASR) on GPT-4 [33] evaluated by the AdvBench dataset [61]”
From Jailbreak and Guard Aligned Language Models With Only Few In-Context Demonstrations (2026) · cites this paper as [61]
Section 3 describes noisy-label experiments using GPT-2 models, not an ICA attack on GPT-4. Searches for ICA, an 81% attack success rate, and AdvBench found no passage in our copy reporting the claimed result. The indexed passages provide section labels but no page numbers. (LLM, conf=0.75)
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Self-citation
- Authors
- Cheng, C., Yu, X., Wen, H., Sun, J., Yue, G., Zhang, Y., & Wei, Z.
- Affiliations
- ShanghaiTech University
- Fudan University (CN)
- Xi'an Jiaotong University (CN)
- Peking University (CN)
- Venue
- ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2025-04-06
- Pages
- 1-5
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4408355401
- Cited by
- 11
- References
- 42
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 2
- FWCI
- 8.58
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- in-context learning, label noise, label noise robustness, transformer models, large language models, data augmentation, inference robustness
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