30 citations · 37 across the 14 of their papers we have counts for
5 papers · 1 filter
CurveShift: Is Agent Progress Scalar? Separating Level from Shape
Hanwen Xing, Pengyun Wang, BingXu Meng +8
Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summa…
Disparities In Negation Understanding Across Languages In Vision-Language Models
Charikleia Moraitaki, Sarah Pan, Skyler Pulling +3
Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ("X is present") even when the correct description contains negation ("no…
Train Long, Think Short: Curriculum Learning for Efficient Reasoning
Hasan Abed Al Kader Hammoud, Kumail Alhamoud, Abed Hammoud +3
Recent work on enhancing the reasoning abilities of large language models (LLMs) has introduced explicit length control as a means of constraining computational cost while preservi…
MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering
Yuexing Hao, Kumail Alhamoud, Hyewon Jeong +6
Large Language Models (LLMs) have demonstrated remarkable performance on various medical question-answering (QA) benchmarks, including standardized medical exams. However, correct…
Medical Hallucinations in Foundation Models and Their Impact on Healthcare
Yubin Kim, Hyewon Jeong, Shan Chen +24
Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence an…