Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
A mechanistic interpretation of arithmetic reasoning in language models using causal mediation analysis.arXiv preprint arXiv:2305.15054
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Transformers store count information internally but cannot read it out as digits due to near-orthogonal alignment with output-head rows; updating digit rows or applying LoRA to attention layers improves constrained and unconstrained counting respectively.
Proficient LLMs detect arithmetic tasks early but output correct answers only in final layers, with attention and MLP modules dividing labor in a way absent from less proficient models.
LLMs and LVLMs encode latent positional count information in individual tokens or visual features, with an internal counter mechanism that updates per item and emerges progressively across layers, relying on structural cues like separators.
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
Activation patching provides evidence about neural network circuits when the choice of metric is aligned with the hypothesis and common interpretation errors are avoided.
Varying evaluation metrics and corruption methods in activation patching produces different localization and circuit discovery outcomes in language models, leading to recommendations for preferred practices.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It
Transformers store count information internally but cannot read it out as digits due to near-orthogonal alignment with output-head rows; updating digit rows or applying LoRA to attention layers improves constrained and unconstrained counting respectively.
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Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms
Proficient LLMs detect arithmetic tasks early but output correct answers only in final layers, with attention and MLP modules dividing labor in a way absent from less proficient models.
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Understanding Counting Mechanisms in Large Language and Vision-Language Models
LLMs and LVLMs encode latent positional count information in individual tokens or visual features, with an internal counter mechanism that updates per item and emerges progressively across layers, relying on structural cues like separators.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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How to use and interpret activation patching
Activation patching provides evidence about neural network circuits when the choice of metric is aligned with the hypothesis and common interpretation errors are avoided.
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Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
Varying evaluation metrics and corruption methods in activation patching produces different localization and circuit discovery outcomes in language models, leading to recommendations for preferred practices.