Callison-Burch, Chris

CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

Majumder, B. P., Mishra, B. D., Jansen, P., Tafjord, O., Tandon, N., Zhang, L., Callison-Burch, C., & Clark, P. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 4

Large Language Models Can Self-Improve At Web Agent Tasks

Patel, A., Hofmarcher, M., Leoveanu-Condrei, C., Dinu, M.-C., Callison-Burch, C., & Hochreiter, S. (2024) · arXiv

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 1

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

Dugan, L., Hwang, A., Trhlik, F., Ludan, J. M., Zhu, A., Xu, H., Ippolito, D., & Callison-Burch, C. (2024) · arXiv

Authorship Attribution and ProfilingAdversarial Robustness in Machine Learning

Cited by 1

Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why

Rao, D., & Callison-Burch, C. (2026) · arXiv

Reliability and Agreement in MeasurementComputational and Text Analysis Methods

Cited by 0

Decision Models vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places

Rao, D., & Callison-Burch, C. (2026) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0