62 citations · 104 across the 24 of their papers we have counts for
6 papers · 2 filters
Culture Cartography: Mapping the Landscape of Cultural Knowledge
Caleb Ziems, William Held, Jane Yu +3
To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find such knowledge that is (1) salient…
Relative Scaling Laws for LLMs
William Held, David Hall, Percy Liang +1
Scaling laws describe how language models improve with additional data, parameters, and compute. While widely used, they are typically measured on aggregate test sets. Aggregate ev…
AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation
Potsawee Manakul, Woody Haosheng Gan, Michael J. Ryan +5
Current speech evaluation suffers from two critical limitations: the need and difficulty of designing specialized systems targeting individual audio characteristics, and poor corre…
SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs
Michael J Ryan, Omar Shaikh, Aditri Bhagirath +3
Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models…
Mind the Gap! Static and Interactive Evaluations of Large Audio Models
Minzhi Li, William Barr Held, Michael J Ryan +4
As AI chatbots become ubiquitous, voice interaction presents a compelling way to enable rapid, high-bandwidth communication for both semantic and social signals. This has driven re…
Optimizing Pretraining Data Mixtures with LLM-Estimated Utility
William Held, Bhargavi Paranjape, Punit Singh Koura +3
Large Language Models improve with increasing amounts of high-quality training data. However, leveraging larger datasets requires balancing quality, quantity, and diversity across…