Xen and the Art of Repeated Research. - Bryan Clark; Todd Deshane; Eli M. Dow; Stephen Evanchik; Matthew Finlayson; Jason Herne; Jeanna Neefe Matthews (USENIX ATC, FREENIX Track, 2004)

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models. - Matthew Finlayson; Aaron Mueller; Sebastian Gehrmann; Stuart M. Shieber; Tal Linzen; Yonatan Belinkov (ACL/IJCNLP (1), 2021)

What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment. - Matthew Finlayson; Kyle Richardson 001; Ashish Sabharwal; Peter Clark (EMNLP, 2022)

LILA: A Unified Benchmark for Mathematical Reasoning. - Swaroop Mishra; Matthew Finlayson; Pan Lu; Leonard Tang; Sean Welleck; Chitta Baral; Tanmay Rajpurohit; Oyvind Tafjord; Ashish Sabharwal; Peter Clark; Ashwin Kalyan (EMNLP, 2022)

Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy. - Sarah Wiegreffe; Matthew Finlayson; Oyvind Tafjord; Peter Clark; Ashish Sabharwal (EMNLP, 2023)

Decomposed Prompting: A Modular Approach for Solving Complex Tasks. - Tushar Khot; Harsh Trivedi; Matthew Finlayson; Yao Fu; Kyle Richardson 001; Peter Clark; Ashish Sabharwal (ICLR, 2023)

Attentiveness to Answer Choices Doesn't Always Entail High QA Accuracy. - Sarah Wiegreffe; Matthew Finlayson; Oyvind Tafjord; Peter Clark; Ashish Sabharwal (CoRR, 2023)

Closing the Curious Case of Neural Text Degeneration. - Matthew Finlayson; John Hewitt; Alexander Koller; Swabha Swayamdipta; Ashish Sabharwal (ICLR, 2024)

Logits of API-Protected LLMs Leak Proprietary Information. - Matthew Finlayson; Xiang Ren 001; Swabha Swayamdipta (CoRR, 2024)

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models. - Sean Welleck; Amanda Bertsch; Matthew Finlayson; Hailey Schoelkopf; Alex Xie; Graham Neubig; Ilia Kulikov; Zaïd Harchaoui (Trans. Mach. Learn. Res., 2024)

Post-training an LLM for RAG? Train on Self-Generated Demonstrations. - Matthew Finlayson; Ilia Kulikov; Daniel M. Bikel; Barlas Oguz; Xilun Chen 002; Aasish Pappu (CoRR, 2025)

Teaching Models to Understand (but not Generate) High-risk Data. - Ryan Wang; Matthew Finlayson; Luca Soldaini; Swabha Swayamdipta; Robin Jia (CoRR, 2025)

Better Language Model Inversion by Compactly Representing Next-Token Distributions. - Murtaza Nazir; Matthew Finlayson; John X. Morris; Xiang Ren 001; Swabha Swayamdipta (CoRR, 2025)