Summary of Exploring the Robustness Of In-context Learning with Noisy Labels, by Chen Cheng et al.
Exploring the Robustness of In-Context Learning with Noisy Labels
by Chen Cheng, Xinzhi Yu, Haodong Wen, Jingsong Sun, Guanzhang Yue, Yihao Zhang, Zeming Wei
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent phenomenon of In-Context Learning (ICL) in Transformer architectures, particularly large language models (LLMs), has garnered significant attention. However, the ability of Transformers to learn in the presence of noisy samples remains understudied. This paper investigates the robustness of Transformers against noisy labels during ICL, showing notable resilience against diverse types of noise in demonstration labels. The authors also explore whether introducing noise into the training set enhances such robustness and find that it can improve the robustness of ICL. The study provides valuable insights into the research on Transformers in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers have a special ability called In-Context Learning (ICL) that helps them learn new things quickly. But what happens when they’re given noisy information? This paper looks at how well Transformers can handle noise and find that they do pretty well, even with very different types of noise. They also test whether adding some noise to the training data makes a difference, and it does! The results help us understand how Transformers work better. |
Keywords
» Artificial intelligence » Attention » Natural language processing » Transformer
