| Challenge: | Existing interpretability methods exhibit structural limitations in the field of deception detection. |
| Approach: | They propose a method to capture layerwise activations at sparse "decision points" . they capture deception as a dynamic process, a trajectory through the model's hidden-state space . |
| Outcome: | The proposed classifier achieves comparable performance to PCA-reduced probing for binary sycophancy detection and shows preliminary utility for 4-way deception-type classification. |
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| Challenge: | Existing explainability methods for Large Language Models treat hidden states as static points in activation space, but they are saturated with polysemantic features. |
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Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings (2025.acl-long)
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| Challenge: | Detecting deception in an increasingly digital world is a critical and challenging task. |
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When Truthful Representations Flip Under Deceptive Instructions? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) follow maliciously crafted instructions to generate deceptive responses, posing safety challenges. |
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