HDMI is a new probe-free technique that steers LLM hidden states via margin objectives to achieve more reliable causal interventions than prior probe-based methods on standard benchmarks.
arXiv preprint arXiv:2311.04897 , year=
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NITP adds dense supervision from shallow model layers to predict implicit next-token semantics, yielding consistent downstream gains on 0.5B-9B models with ~2% extra training FLOPs.
HyperLens reveals that deeper transformer layers magnify small confidence changes into fine-grained trajectories, allowing quantification of cognitive effort where complex tasks demand more and standard SFT can reduce it.
citing papers explorer
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Inference Time Causal Probing in LLMs
HDMI is a new probe-free technique that steers LLM hidden states via margin objectives to achieve more reliable causal interventions than prior probe-based methods on standard benchmarks.
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NITP: Next Implicit Token Prediction for LLM Pre-training
NITP adds dense supervision from shallow model layers to predict implicit next-token semantics, yielding consistent downstream gains on 0.5B-9B models with ~2% extra training FLOPs.
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HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory
HyperLens reveals that deeper transformer layers magnify small confidence changes into fine-grained trajectories, allowing quantification of cognitive effort where complex tasks demand more and standard SFT can reduce it.