MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
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A conjecture-then-validate method lets LLMs convert opaque lexical cues from deceptive-review classifiers into interpretable language phenomena that are empirically grounded and more predictive than direct LLM outputs.
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MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection
MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
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Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues
A conjecture-then-validate method lets LLMs convert opaque lexical cues from deceptive-review classifiers into interpretable language phenomena that are empirically grounded and more predictive than direct LLM outputs.