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Summary of Joint Training For Selective Prediction, by Zhaohui Li et al.


Joint Training for Selective Prediction

by Zhaohui Li, Rebecca J. Passonneau

First submitted to arxiv on: 31 Oct 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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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
This research paper proposes a novel approach for selective prediction (SP) in natural language processing (NLP). SP methods determine when to rely on machine learning models versus seeking human input. The authors focus on improving softmax as a measure of model confidence and develop a joint-training method that optimizes learned representations used by the classifier module and a learned deferral policy. This approach outperforms two strong baselines in four classification tasks, not only enhancing SP outcomes but also improving the overall performance of both modules.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about making machine learning models work better with humans. Right now, these models can be very accurate, but they’re not always right. To make them more trustworthy, we need to figure out when it’s best to use their predictions and when we should ask a human for help. The authors have come up with a new way of doing this that involves training two parts together: the part that makes predictions and the part that decides when to ask for human help. This approach works better than other methods in four different tasks, which means it can be used in lots of real-world situations.

Keywords

* Artificial intelligence  * Classification  * Machine learning  * Natural language processing  * Nlp  * Softmax