Public articles linked to the same research event.
arXiv Addressing hallucinations in Multimodal Large Language Models (MLLMs), this work proposes Causal-Invariant Masking (CIM), which measures the semantic shift between original predictions and those conditioned on a causally-focused view to decompose uncertainty types, introduces Semantic Divergence as the core UQ metric with theoretical evidence that it converges to the variance of the model's sensitivity to non-causal correlations, and further proposes Expected Embedding Drift (EED), a fast geometric proxy metric; experiments report state-of-the-art performance on various benchmarks, with EED accelerating by nearly 50% at comparable performance.
Addressing hallucinations in Multimodal Large Language Models (MLLMs), this work proposes Causal-Invariant Masking (CIM), which measures the semantic shift between original predictions and those conditioned on a causally-focused view to decompose uncertainty types, introduces Semantic Divergence as the core UQ metric with theoretical evidence that it converges to the variance of the model's sensitivity to non-causal correlations, and further proposes Expected Embedding Drift (EED), a fast geometric proxy metric; experiments report state-of-the-art performance on various benchmarks, with EED accelerating by nearly 50% at comparable performance.
Addressing hallucinations in Multimodal Large Language Models (MLLMs), this work proposes Causal-Invariant Masking (CIM), which measures the semantic shift between original predictions and those conditioned on a causally-focused view to decompose uncertainty types, introduces Semantic Divergence as the core UQ metric with theoretical evidence that it converges to the variance of the model's sensitivity to non-causal correlations, and further proposes Expected Embedding Drift (EED), a fast geometric proxy metric; experiments report state-of-the-art performance on various benchmarks, with EED accelerating by nearly 50% at comparable performance.
Addressing hallucinations in Multimodal Large Language Models (MLLMs), this work proposes Causal-Invariant Masking (CIM), which measures the semantic shift between original predictions and those conditioned on a causally-focused view to decompose uncertainty types, introduces Semantic Divergence as the core UQ metric with theoretical evidence that it converges to the variance of the model's sensitivity to non-causal correlations, and further proposes Expected Embedding Drift (EED), a fast geometric proxy metric; experiments report state-of-the-art performance on various benchmarks, with EED accelerating by nearly 50% at comparable performance.