Josh Magnus Ludan — accepted AI-conference papers

2025

A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design

Haydn Thomas Jones, Natalie Maus, Josh magnus Ludan, Maggie Ziyu Huan, Jiaming Liang, Marcelo Der Torossian Torres +6

NeurIPS 2025poster

AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis across diverse models on the GuacaMol bench…

Cited by 0 Source ↗Scholar ↗

2024

RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors

Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu +2

ACL 2024long

Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shared benchmark datasets and even when they are, the datasets used for evaluation are insufficiently challenging—lacking va…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗

2023

Explanation-based Finetuning Makes Models More Robust to Spurious Cues

Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki +1

ACL 2023long

Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data. We propose explanation-based finetuning as a general approach to mitigate LLMs’ reliance on spu…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗