1 citations · 2 across the 15 of their papers we have counts for
7 papers · 1 filter
LLMs Know the Constraint But Do Not Use It: Activation Bottlenecks in Pragmatic Constraint Reasoning
Yubo Li, Ramayya Krishnan, Rema Padman
When a salient surface cue competes with an implicit feasibility constraint, LLMs often fail -- but aggregate accuracy conflates genuine constraint inference with conservative defa…
Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG
Yubo Li, Rema Padman, Ramayya Krishnan
A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depending on which source it retrie…
The Model Says Walk: Measuring whether LLMs Condition on Hidden Constraints
Yubo Li, Lu Zhang, Tianchong Jiang +2
Asked whether to walk or drive to a car wash 50 m away, most language models say walk, forgetting that the car has to be there. Such failures are usually measured by accuracy on qu…
Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Yubo Li, Ramayya Krishnan, Rema Padman
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks…
Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models
Yubo Li, Xiaobin Shen, Yidi Miao +4
Recent advances in large language models (LLMs) have substantially improved single-turn task performance, yet real-world applications increasingly demand sophisticated multi-turn i…
Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspective
Xinyu Yao, Aditya Sannabhadti, Holly Wiberg +2
Medical knowledge graphs (KGs) are essential for clinical decision support and biomedical research, yet they often exhibit incompleteness due to knowledge gaps and structural limit…
Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions
Yubo Li, Yidi Miao, Xueying Ding +2
Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent and coherent behavior across mu…