12 citations · 21 across the 18 of their papers we have counts for
7 papers · 1 filter
Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents
Priyanshu Kumar, Elaine Lau, Saranya Vijayakumar +9
For safety reasons, large language models (LLMs) are trained to refuse harmful user instructions, such as assisting dangerous activities. We study an open question in this work: do…
Planning In Natural Language Improves LLM Search For Code Generation
Evan Wang, Federico Cassano, Catherine Wu +7
While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize tha…
Revisiting the Superficial Alignment Hypothesis
Mohit Raghavendra, Vaskar Nath, Sean Hendryx
The Superficial Alignment Hypothesis posits that almost all of a language model's abilities and knowledge are learned during pre-training, while post-training is about giving a mod…
Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Data
Spencer Whitehead, Jacob Phillips, Sean Hendryx
Multimodal language models can exhibit hallucinations in their outputs, which limits their reliability. The ability to automatically detect these errors is important for mitigating…
Learning Goal-Conditioned Representations for Language Reward Models
Vaskar Nath, Dylan Slack, Jeff Da +4
Techniques that learn improved representations via offline data or self-supervised objectives have shown impressive results in traditional reinforcement learning (RL). Nevertheless…
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Hugh Zhang, Jeff Da, Dean Lee +12
Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actuall…
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
Will LeVine, Benjamin Pikus, Jacob Phillips +3
As deep neural networks become adopted in high-stakes domains, it is crucial to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely…