No-regret Language Generation
We introduce no-regret generation, where a generator makes a sublinear number of mistakes uniformly over all demonstration sequences and compatible target languages, and extend …
Robust Detection of LLM-Generated Text under Contamination
Studies reliable detection under text contamination. I independently designed the benchmark and implemented the models, experimental pipelines, and Python evaluations, including …