VISE is the first benchmark for sycophancy in Video-LLMs, with two training-free mitigation strategies based on key-frame selection and internal representation steering.
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Rule knowledge in LLMs is localized by form across layers; a distributed multi-layer editing method improves instance portability by 13.91 and rule understanding by 50.19 percentage points over baselines on multiple models.
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Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs
VISE is the first benchmark for sycophancy in Video-LLMs, with two training-free mitigation strategies based on key-frame selection and internal representation steering.
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Distributed Multi-Layer Editing for Rule-Level Knowledge in Large Language Models
Rule knowledge in LLMs is localized by form across layers; a distributed multi-layer editing method improves instance portability by 13.91 and rule understanding by 50.19 percentage points over baselines on multiple models.