18 citations · 22 across the 16 of their papers we have counts for
16 papers
Do Audio Language Models Use Paralinguistic Evidence? Counterfactual Audits for Response Evaluation
Kevin Miller, Arjun Chandra, Venkatesh Saligrama
Audio-language models (ALMs) are increasingly used as judges for speech-to-speech systems, but a judge that receives audio may not actually use paralinguistic evidence. We introduc…
A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models
Rahul Gupta, Abhinav Mohanty, Payal Motwani +8
As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan hig…
PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges
Swastik Roy, Rajkumar Pujari, Tharindu Kumarage +5
LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rubric asking for a response to be `…
DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov +3
Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for ge…
Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation
Arjun Chandra, Kevin Miller, Venkatesh Ravichandran +2
Large Language Model (LLM) judges exhibit strong reasoning capabilities but are limited to textual content. This leaves current automatic Speech-to-Speech (S2S) evaluation methods…
BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models
Shengao Wang, Wenqi Wang, Zecheng Wang +20
Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded…