Summary of Efficient Nearest Neighbor Based Uncertainty Estimation For Natural Language Processing Tasks, by Wataru Hashimoto et al.
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
by Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel method called k-Nearest Neighbor Uncertainty Estimation (kNN-UE) to improve trustworthiness in deep neural network predictions. The proposed method considers not only distances from neighbors but also label ratios, addressing issues of uncertainty estimation and miscalibration. Empirical results on sentiment analysis, natural language inference, and named entity recognition demonstrate kNN-UE’s superiority over baselines and density-based methods in various calibration and uncertainty metrics. The study also explores the trade-off between inference overhead and uncertainty estimation performance when combining approximate nearest neighbor search techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computer predictions more reliable. It proposes a new way to measure how sure a prediction is, called kNN-UE. This method looks at not just how similar other examples are, but also what they’re labeled as. The authors tested this method on three tasks: understanding sentiment, recognizing entities, and drawing conclusions about text. They found that their method outperformed others in several ways. Additionally, the study shows that using a shortcut to find nearby examples can balance the need for fast predictions with the importance of accurate uncertainty estimation. |
Keywords
* Artificial intelligence * Inference * Named entity recognition * Nearest neighbor * Neural network
