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Oam Patel

2 accepted papers

2026

Priors in time: Missing inductive biases for language model interpretability

ICLR 2026poster

A central aim of interpretability tools applied to language models is to recover meaningful concepts from model activations. Existing feature extraction methods focus on single activations regardless of the context, implicitly assuming independence (and therefore stationarity). This leaves open whet…

Cited by 0SourcecodeScholar
2023

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model

NeurIPS 2023spotlight

We introduce Inference-Time Intervention (ITI), a technique designed to enhance the "truthfulness" of large language models (LLMs). ITI operates by shifting model activations during inference, following a learned set of directions across a limited number of attention heads. This intervention signifi…