3 papers
cs.LG2025
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…
cs.LG2024
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…
cs.LG2024
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…