Daniel McFarland — accepted AI-conference papers

2024

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, Haley Lepp, Hancheng Cao, Xuandong Zhao +6

ICML 2024oral

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 likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-wor…

Cited by 122 Source ↗PDF ↗code ↗Scholar ↗

2023

Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies

Raymond Zhang, Neha Nayak Kennard, Daniel Smith, Daniel McFarland, Andrew McCallum, Katherine Keith

ACL 2023findings

A promising approach to estimate the causal effects of peer review policies is to analyze data from publication venues that shift policies from single-blind to double-blind from one year to the next. However, in these settings the content of the manuscript is a confounding variable—each year has a d…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗