14 citations · 32 across the 17 of their papers we have counts for
4 papers · 1 filter
Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability
Taylor Sorensen, Benjamin Newman, Jared Moore +5
Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible v…
Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel +4
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic exe…
Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen +6
High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data…
Political Neutrality in AI Is Impossible- But Here Is How to Approximate It
Jillian Fisher, Ruth E. Appel, Chan Young Park +9
AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for…