617 citations · 1.8k across the 79 of their papers we have counts for
12 papers · 1 filter
Base Models Look Human To AI Detectors
Yixuan Even Xu, Ziqian Zhong, Aditi Raghunathan +2
As AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-integrity workflows. We report…
Compressed Sensing for Capability Localization in Large Language Models
Anna Bair, Yixuan Even Xu, Mingjie Sun +1
Large language models (LLMs) exhibit a wide range of capabilities, including mathematical reasoning, code generation, and linguistic behaviors. We show that Transformer architectur…
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
Satyapriya Krishna, Andy Zou, Rahul Gupta +6
The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing…
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao +4
Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the…
Unnatural Languages Are Not Bugs but Features for LLMs
Keyu Duan, Yiran Zhao, Zhili Feng +9
Large Language Models (LLMs) have been observed to process non-human-readable text sequences, such as jailbreak prompts, often viewed as a bug for aligned LLMs. In this work, we pr…
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
Daniele Paliotta, Junxiong Wang, Matteo Pagliardini +6
Recent advancements have demonstrated that the performance of large language models (LLMs) can be significantly enhanced by scaling computational resources at test time. A common s…