3 citations · 3 across the 10 of their papers we have counts for
12 papers · 1 filter
On the Shelf Life of Fine-Tuned LLM-Judges: Future-Proofing, Backward-Compatibility, and Question Generalization
Janvijay Singh, Austin Xu, Yilun Zhou +3
The LLM-as-a-judge paradigm is widely used in both evaluating free-text model responses and reward modeling for model alignment and fine-tuning. Recently, fine-tuning judges with j…
Variation in Verification: Understanding Verification Dynamics in Large Language Models
Yefan Zhou, Austin Xu, Yilun Zhou +3
Recent advances have shown that scaling test-time computation enables large language models (LLMs) to solve increasingly complex problems across diverse domains. One effective para…
MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision
Zixuan Ke, Austin Xu, Yifei Ming +4
Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS de…
SMILE: A Composite Lexical-Semantic Metric for Question-Answering Evaluation
Shrikant Kendre, Austin Xu, Honglu Zhou +3
Traditional evaluation metrics for textual and visual question answering, like ROUGE, METEOR, and Exact Match (EM), focus heavily on n-gram based lexical similarity, often missing…
Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains
Austin Xu, Xuan-Phi Nguyen, Yilun Zhou +3
Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, re…
Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms
Shrey Pandit, Xuan-Phi Nguyen, Yifei Ming +4
Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools. These tasks remain challenging, as the underly…