1 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Not All LLM Reasoning is Visible in the Chain-of-Thought
Vatsal Baherwani, Tom Goldstein, Ashwinee Panda
A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit i…
Speculating Experts Accelerates Inference for Mixture-of-Experts
Vivan Madan, Prajwal Singhania, Abhinav Bhatele +2
Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token…
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
Rishab Alagharu, Ishneet Sukhvinder Singh, Shaibi Shamsudeen +2
Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish differe…
Multi-Token Prediction via Self-Distillation
John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson +3
Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inferen…