Summary of Self-knowledge Distillation For Learning Ambiguity, by Hancheol Park et al.
Self-Knowledge Distillation for Learning Ambiguity
by Hancheol Park, Soyeong Jeong, Sukmin Cho, Jong C. Park
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed self-knowledge distillation method aims to improve natural language understanding (NLU) by enabling models to learn accurate label distributions. The approach leverages knowledge from lower layers and includes a learning phase that re-calibrates confidence for ambiguous samples. Experimental results demonstrate the effectiveness of this method on diverse NLU benchmark datasets, producing better label distributions and alleviating over-confidence issues. This is particularly significant as it generates better distributions than existing state-of-the-art methods while being more efficient in training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to help language models make better predictions when they’re not sure what the correct answer is. Right now, these models can be really confident, even if they’re wrong. This approach helps them learn from their mistakes and become less over-confident. It works by having the model teach itself about ambiguity in the data it’s trained on. The results show that this method does a better job than other approaches at producing accurate predictions and reducing over-confidence. |
Keywords
» Artificial intelligence » Knowledge distillation » Language understanding
