CITATION.cff

cff-version: 1.2.0
title: >-
  BEARS Make Neuro-Symbolic Models Aware Of Their Reasoning
  Shortcuts
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Emanuele
    family-names: Marconato
    email: emanuele.marconato@unitn.it
    affiliation: University of Trento
  - given-names: Samuele
    family-names: Bortolotti
    email: samuele.bortolotti@unitn.it
    affiliation: University of Trento
  - given-names: Emile
    family-names: van Krieken
    email: Emile.van.Krieken@ed.ac.uk
    affiliation: University of Edinburgh
  - given-names: Antonio
    family-names: Vergari
    email: avergari@ed.ac.uk
    affiliation: University of Edinburgh
  - given-names: Andrea
    family-names: Passerini
    email: andrea.passerini@unitn.it
    affiliation: University of Trento
  - given-names: Stefano
    family-names: Teso
    email: stefano.teso@unitn.it
    affiliation: University of Trento
identifiers:
  - type: url
    value: 'https://arxiv.org/abs/2402.12240'
repository-code: 'https://github.com/samuelebortolotti/bears'
url: 'https://samuelebortolotti.github.io/bears'
abstract: >-
  Neuro-Symbolic (NeSy) predictors that conform to symbolic
  knowledge - encoding, e.g., safety constraints - can be
  affected by Reasoning Shortcuts (RSs): They learn concepts
  consistent with the symbolic knowledge by exploiting
  unintended semantics. RSs compromise reliability and
  generalization and, as we show in this paper, they are
  linked to NeSy models being overconfident about the
  predicted concepts. Unfortunately, the only trustworthy
  mitigation strategy requires collecting costly dense
  supervision over the concepts. Rather than attempting to
  avoid RSs altogether, we propose to ensure NeSy models are
  aware of the semantic ambiguity of the concepts they
  learn, thus enabling their users to identify and distrust
  low-quality concepts. Starting from three simple
  desiderata, we derive bears (BE Aware of Reasoning
  Shortcuts), an ensembling technique that calibrates the
  model's concept-level confidence without compromising
  prediction accuracy, thus encouraging NeSy architectures
  to be uncertain about concepts affected by RSs. We show
  empirically that bears improves RS-awareness of several
  state-of-the-art NeSy models, and also facilitates
  acquiring informative dense annotations for mitigation
  purposes. 
keywords:
  - Neuro-Symbolic AI
  - Uncertainty
  - Reasoning Shortcuts
  - Calibration
  - Probabilistic Reasoning
  - Concept-Based Models
date-released: '2024-02-19'