6 papers · 1 filter
Self-Trained Verification for Training- and Test-Time Self-Improvement
Chen Henry Wu, Aditi Raghunathan
Self-improvement at scale has been a longstanding goal for reasoning models, and there are two natural places to do it: at test time, through verification-refinement (V-R) loops; a…
Pando: Do Interpretability Methods Work When Models Won't Explain Themselves?
Ziqian Zhong, Aashiq Muhamed, Mona T. Diab +2
Mechanistic interpretability is often motivated for alignment auditing, where a model's verbal explanations can be absent, incomplete, or misleading. Yet many evaluations do not co…
ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases
Ziqian Zhong, Aditi Raghunathan, Nicholas Carlini
The tendency to find and exploit "shortcuts" to complete tasks poses significant risks for reliable assessment and deployment of large language models (LLMs). For example, an LLM a…
Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs
Ziqian Zhong, Aditi Raghunathan
The releases of powerful open-weight large language models (LLMs) are often not accompanied by access to their full training data. Existing interpretability methods, particularly t…
Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions
Jhih-Yi Hsieh, Aditi Raghunathan, Nihar B. Shah
In the peer review process of top-tier machine learning (ML) and artificial intelligence (AI) conferences, reviewers are assigned to papers through automated methods. These assignm…
Scaling Laws for Precision
Tanishq Kumar, Zachary Ankner, Benjamin F. Spector +6
Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware…