3 citations · 3 across the 5 of their papers we have counts for
6 papers
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
Jason Luo, Saibilila Abudukelimu, Judy Song +4
Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters…
HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +7
Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills and the limitations of exis…
RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
Andrew Zhuoer Feng, Cunxiang Wang, Bosi Wen +4
Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often costly to train an…
RAVEL: Reasoning Agents for Validating and Evaluating LLM Text Synthesis
Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo +9
Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation frameworks lack t…
PromptGAR: Flexible Promptive Group Activity Recognition
Zhangyu Jin, Andrew Feng, Ankur Chemburkar +1
We present PromptGAR, a novel framework for Group Activity Recognition (GAR) that offering both input flexibility and high recognition accuracy. The existing approaches suffer from…
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation
Pei Ke, Bosi Wen, Zhuoer Feng +9
Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing…