3 papers
cs.AI2026
Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback
Giulia Pucci, Emily Hemendinger, Ruizhe Li +3
Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. A…
cs.CL2025
"I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?
Jiazhou Ji, Jie Guo, Weidong Qiu +6
Distinguishing between human- and LLM-generated texts is crucial given the risks associated with misuse of LLMs. This paper investigates detection and explanation capabilities of c…
cs.CL2025
Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated
Jiazhou Ji, Ruizhe Li, Shujun Li +6
As LLMs rapidly advance, increasing concerns arise regarding risks about actual authorship of texts we see online and in real world. The task of distinguishing LLM-authored texts i…