DiaVLo: Diagnosing Behaviours of Vision-Language Models
DiaVLo diagnoses vision-language model behaviours, comparing desired and observed actions and estimating influential concepts.
DiaVLo is a diagnostic framework for vision-language models that uses human curation and the models' own generation to specify desired and observed behaviours and expose misalignment. It also estimates which concepts most strongly steer those behaviours. Evaluations on several open-source VLMs under classification and generation show that the resulting behaviour labels correlate with measured performance and add context to those scores. The method surfaces clearly aligned and misaligned behaviours plus patterns in how models perceive, organize, and prioritize concepts.
- Combines human curation with model generation to specify desired and observed behaviours.
- Provides causal estimates of concepts that most influence VLM behaviour.
- Labels correlate with performance in classification and generation settings.
- Experiments surface both aligned and misaligned behaviours in open-source VLMs.
From the original article
1 min read at arxiv.orgVision-language models (VLMs) rely on storing and transferring appropriate information across their sub-components. Verifying that the VLMs exhibit desired behaviours, while avoiding harmful ones, is central to their reliable deployment. Yet, methods that identify VLM behaviours remain scarce.
By Lorenzo Corti · arxiv.org
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