8 papers
Reasoning Fine-Tuning Induces Persistent Latent Policy States
Abir Harrasse, Michael Lan, Hunar Batra +2
Reasoning-specialized language models show large performance gains over base models, yet the internal changes responsible for improved multi-step reasoning remain poorly understood…
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Rishit Dagli, Abir Harrasse, Luke Zhang +4
Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model chan…
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Florent Draye, Abir Harrasse, Vedant Palit +8
Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information. Recent approaches based on dictionary learning and transcoders…
Curveball Steering: The Right Direction To Steer Isn't Always Linear
Shivam Raval, Hae Jin Song, Linlin Wu +4
Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing methods largely rely on the L…
Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders
Abir Harrasse, Florent Draye, Punya Syon Pandey +2
Multilingual Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear. Do they form shared multilingual representat…
Debate, Deliberate, Decide (D3): A Cost-Aware Adversarial Framework for Reliable and Interpretable LLM Evaluation
Abir Harrasse, Chaithanya Bandi, Hari Bandi
The evaluation of Large Language Models (LLMs) remains challenging due to inconsistency, bias, and the absence of transparent decision criteria in automated judging. We present Deb…