5 papers
Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment
Woody Haosheng Gan, William Held, Diyi Yang
The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate wh…
Scaling Open Discrete Audio Foundation Models with Interleaved Semantic, Acoustic, and Text Tokens
Potsawee Manakul, Woody Haosheng Gan, Martijn Bartelds +3
Current audio language models are predominantly text-first, either extending pre-trained text LLM backbones or relying on semantic-only audio tokens, limiting general audio modelin…
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…