activity
20232026
most citedWhen AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

2 citations · 5 across the 18 of their papers we have counts for

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12 papers · 1 filter

cs.CL2026

Summarization is Not Dead Yet

Dongqi Liu, Chenxi Whitehouse, Zheng Zhao +3

The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summar…

cs.CL2026

Text-to-Stage: Spatial Layouts from Long-form Narratives

Jefferson Hernandez, Swarnadeep Saha, Chenxi Whitehouse +6

In this work, we probe the ability of a language model to demonstrate spatial reasoning from unstructured text, mimicking human capabilities and automating a process that benefits…

cs.CL2026

APRES: An Agentic Paper Revision and Evaluation System

Bingchen Zhao, Jenny Zhang, Chenxi Whitehouse +8

Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or…

cs.CL2026

Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling

Alaa Elsetohy, Sama Hadhoud, Haryo Akbarianto Wibowo +4

Multilingual benchmarks rarely test reasoning over culturally grounded premises: translated datasets keep English-centric scenarios, while culture-first datasets often lack control…

cs.CL2025

MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Chenxi Whitehouse, Sebastian Ruder, Tony Lin +6

Ensuring native-like quality of large language model (LLM) responses across many languages is challenging. To address this, we introduce MENLO, a framework that operationalizes the…

cs.CL2025

J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning

Chenxi Whitehouse, Tianlu Wang, Ping Yu +4

The progress of AI is bottlenecked by the quality of evaluation, making powerful LLM-as-a-Judge models a core solution. The efficacy of these judges depends on their chain-of-thoug…