Conceptual Contrastive Edits in Textual and Vision-Language Retrieval

• URL: http://arxiv.org/abs/2503.01914v1
• Date: Sat, 01 Mar 2025 10:14:28 GMT
• Title: Conceptual Contrastive Edits in Textual and Vision-Language Retrieval
• Authors: Maria Lymperaiou, Giorgos Stamou,
• Abstract summary: We employ post-hoc conceptual contrastive edits to expose noteworthy patterns and biases imprinted in representations of retrieval models.<n>We apply these edits to explain both linguistic and visiolinguistic pre-trained models in a black-box manner.<n>We also introduce a novel metric to assess the per-word impact of contrastive interventions on model outcomes.
• Score: 1.8591405259852054
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As deep learning models grow in complexity, achieving model-agnostic interpretability becomes increasingly vital. In this work, we employ post-hoc conceptual contrastive edits to expose noteworthy patterns and biases imprinted in representations of retrieval models. We systematically design optimal and controllable contrastive interventions targeting various parts of speech, and effectively apply them to explain both linguistic and visiolinguistic pre-trained models in a black-box manner. Additionally, we introduce a novel metric to assess the per-word impact of contrastive interventions on model outcomes, providing a comprehensive evaluation of each intervention's effectiveness.

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