20 papers
Stop Automating Peer Review Without Rigorous Evaluation
Joachim Baumann, Jiaxin Pei, Sanmi Koyejo +1
Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. W…
Responsible Evaluation of AI for Mental Health
Hiba Arnaout, Anmol Goel, H. Andrew Schwartz +13
Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned wi…
Diffusion Language Models Are Natively Length-Aware
Vittorio Rossi, Giacomo Cirò, Davide Beltrame +3
Unlike autoregressive language models, which terminate variable-length generation upon predicting an End-of-Sequence (EoS) token, Diffusion Language Models (DLMs) operate over a fi…
Do Large Language Models Adapt to Language Variation across Socioeconomic Status?
Elisa Bassignana, Mike Zhang, Dirk Hovy +1
Humans adjust their linguistic style to the audience they are addressing. However, the extent to which LLMs adapt to different social contexts is largely unknown. As these models i…
PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors
Donya Rooein, Sankalan Pal Chowdhury, Mariia Eremeeva +4
Recent advances in large language models (LLMs) demonstrate their potential as educational tutors. However, different tutoring strategies benefit different student personalities, a…
Can Reasoning Help Large Language Models Capture Human Annotator Disagreement?
Jingwei Ni, Yu Fan, Vilém Zouhar +6
Variation in human annotation (i.e., disagreements) is common in NLP, often reflecting important information like task subjectivity and sample ambiguity. Modeling this variation is…