Summary of Not Eliminate but Aggregate: Post-hoc Control Over Mixture-of-experts to Address Shortcut Shifts in Natural Language Understanding, by Ukyo Honda et al.
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding
by Ukyo Honda, Tatsushi Oka, Peinan Zhang, Masato Mita
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This study tackles a crucial issue in natural language understanding (NLU) where recent models tend to rely on simple patterns in datasets, known as shortcuts. Shortcuts are based on spurious correlations between labels and latent features present in training data. When faced with distribution shifts at inference time, these shortcut-dependent models can generate inaccurate predictions. To address this limitation, the research community has previously focused on training models that eliminate reliance on shortcuts. In contrast, this study takes a different approach by pessimistically aggregating the predictions of a mixture-of-experts model, assuming each expert captures relatively distinct latent features. Experimental results demonstrate that this post-hoc control significantly enhances the model’s robustness to distribution shifts in shortcuts. Additionally, the study highlights practical advantages and provides analysis and supporting results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how AI models for understanding language can sometimes make mistakes because they rely on simple patterns in data. These patterns are not actually connected to what the text means, but the model uses them anyway. When it comes time to use the model in real life, these shortcuts don’t work anymore and the model makes incorrect predictions. Instead of trying to fix this problem by making models less reliant on shortcuts, this study tries a new approach. It uses multiple smaller models (experts) that are good at capturing different types of information, and then combines their predictions. This helps the model make more accurate predictions even when the patterns it relies on don’t work anymore. |
Keywords
» Artificial intelligence » Inference » Language understanding » Mixture of experts
