3 papers
cs.CL2026
How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability
Shawn Im, Changdae Oh, Zhen Fang +1
Semantic associations such as the link between "bird" and "flew" are foundational for language modeling as they enable models to go beyond memorization and instead generalize and g…
cs.AI2026
Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection
Yongxin Deng, Zhen Fang, Sharon Li +1
Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when…
cs.AI2026
LiveMedBench: A Contamination-Free Medical Benchmark for LLMs with Automated Rubric Evaluation
Zhiling Yan, Dingjie Song, Zhe Fang +4
The deployment of Large Language Models (LLMs) in high-stakes clinical settings demands rigorous and reliable evaluation. However, existing medical benchmarks remain static, suffer…