most citedMonitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

68 citations · 68 across the 1 of their papers we have counts for

collaborators

8 papers

cs.CL202668 cited

Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

Weixin Liang, Zachary Izzo, Yaohui Zhang +9

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum l…

cs.CL2025

Adaptive Self-improvement LLM Agentic System for ML Library Development

Genghan Zhang, Weixin Liang, Olivia Hsu +1

ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing thes…

cs.CL2025

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Weixin Liang, Lili Yu, Liang Luo +8

The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models…

physics.med-ph2025

Automated radiotherapy treatment planning guided by GPT-4Vision

Sheng Liu, Oscar Pastor-Serrano, Yizheng Chen +10

Objective: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple con…

cs.AI2025

Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors

Fan Nie, Lan Feng, Haotian Ye +5

Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often imp…

cs.CL2025

The Widespread Adoption of Large Language Model-Assisted Writing Across Society

Weixin Liang, Yaohui Zhang, Mihai Codreanu +3

The recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-ass…