Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification
Conventional evaluation methods for multimodal LLMs (MLLMs) lack interpretability and are often insufficient to fully disclose significant capability gaps across models. To address this, we introduce AuditDM, an automated framework that actively discovers and rectifies MLLM failure modes by auditing their divergence. AuditDM fine-tunes an MLLM as an auditor via reinforcement learning to generate challenging questions and counterfactual images that maximize disagreement among target models. Once trained, the auditor uncovers diverse, interpretable exemplars that reveal model weaknesses and serve as annotation-free data for rectification. When applied to SoTA models like Gemma-3 and PaliGemma-2, AuditDM discovers more than 20 distinct failure types. Fine-tuning on these discoveries consistently improves all models across 16 benchmarks, and enables a 3B model to surpass its 28B counterpart. Our results suggest that as data scaling hits diminishing returns, targeted model auditing offers an effective path to model diagnosis and improvement.
6944c582fbf17e708e186079
Qihao Liu
6944c582fbf17e708e18607a
Chengzhi Mao
6944c582fbf17e708e18607b
Yaojie Liu
6944c582fbf17e708e18607c
Alan Yuille
6944c582fbf17e708e18607d
Wen-Sheng Chu
Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification
Conventional evaluation methods for multimodal LLMs (MLLMs) lack interpretability and are often insufficient to fully disclose significant capability gaps across models. To address this, we introduce AuditDM, an automated framework that actively discovers and rectifies MLLM failure modes by auditing their divergence. AuditDM fine-tunes an MLLM as an auditor via reinforcement learning to generate challenging questions and counterfactual images that maximize disagreement among target models. Once trained, the auditor uncovers diverse, interpretable exemplars that reveal model weaknesses and serve as annotation-free data for rectification. When applied to SoTA models like Gemma-3 and PaliGemma-2, AuditDM discovers more than 20 distinct failure types. Fine-tuning on these discoveries consistently improves all models across 16 benchmarks, and enables a 3B model to surpass its 28B counterpart. Our results suggest that as data scaling hits diminishing returns, targeted model auditing offers an effective path to model diagnosis and improvement.
AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across benchmarks.
AuditDM multimodal LLMs reinforcement learning challenging questions counterfactual images model weaknesses annotation-free data rectification Gemma-3 PaliGemma-2 benchmark model auditing model diagnosis improvement