Summary of Noisyicl: a Little Noise in Model Parameters Calibrates In-context Learning, by Yufeng Zhao et al.
NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning
by Yufeng Zhao, Yoshihiro Sakai, Naoya Inoue
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, the authors propose a novel approach called NoisyICL to improve In-Context Learning (ICL) performance and calibration. Traditional methods for fine-tuning language models on large datasets require significant computing resources and may not be practical. Instead, the researchers introduce random noises to perturb model parameters, achieving better predictions and fairness on 12 downstream datasets using two different models. The results demonstrate NoisyICL’s effectiveness in producing more accurate and calibrated predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NoisyICL is a new way to make language models work better when learning from specific contexts. Right now, these models can be biased and not very confident in their answers. To fix this, researchers added random noise to the model’s parameters. This helped them get more accurate answers and be more honest about how sure they are. They tested this on 12 different datasets using two types of language models. The results showed that NoisyICL really works well and can help make language models better at learning from context. |
Keywords
» Artificial intelligence » Fine tuning
