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Summary of Bears Make Neuro-symbolic Models Aware Of Their Reasoning Shortcuts, by Emanuele Marconato and Samuele Bortolotti and Emile Van Krieken and Antonio Vergari and Andrea Passerini and Stefano Teso


BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts

by Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso

First submitted to arxiv on: 19 Feb 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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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 paper explores the limitations of Neuro-Symbolic (NeSy) predictors, which learn concepts consistent with symbolic knowledge by exploiting unintended semantics. The authors identify Reasoning Shortcuts (RSs) as a primary issue, leading to overconfidence and compromised reliability. They propose bears (BE Aware of Reasoning Shortcuts), an ensembling technique that calibrates the model’s concept-level confidence without compromising prediction accuracy. This approach enables NeSy architectures to be uncertain about concepts affected by RSs. The authors demonstrate the effectiveness of bears on several state-of-the-art NeSy models, facilitating the acquisition of informative dense annotations for mitigation purposes.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about how certain AI models can make mistakes because they rely too much on shortcuts instead of understanding things properly. These models are called Neuro-Symbolic predictors and they learn by finding patterns in data that aren’t actually there. This makes them overconfident and less reliable. The authors suggest a way to fix this problem by making the models more aware of when they’re using these shortcuts. They tested their approach on several different AI models and showed that it improves their performance.

Keywords

* Artificial intelligence  * Semantics