3 citations · 5 across the 11 of their papers we have counts for
11 papers
Implicit Reasoning Steering via Concept Chaining
Xiao Ye, Sanika Chavan, Yuxi Huang +4
Large language models often appear to reason reliably, yet on many questions repeated sampling yields both correct and incorrect answers, revealing an underlying fragility in how f…
Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Xiao Ye, Jacob Dineen, Evan Zhu +3
Forecasters are evaluated by backtesting, which replays resolved questions and grades the probability the system would have assigned before the outcome was known. For LLMs, two cha…
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
Zixuan Gu, Xiaojun Ye, Yang Liu
Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high compu…
Unbiased Visual Reasoning with Controlled Visual Inputs
Zhaonan Li, Shijie Lu, Fei Wang +11
End-to-end Vision-language Models (VLMs) often answer visual questions by exploiting spurious correlations instead of causal visual evidence, and can become more shortcut-prone whe…
CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
Zijun Gao, Zhikun Xu, Xiao Ye +1
Large language models (LLMs) often solve challenging math exercises yet fail to apply the concept right when the problem requires genuine understanding. Popular Reinforcement Learn…
Human-Level and Beyond: Benchmarking Large Language Models Against Clinical Pharmacists in Prescription Review
Yan Yang, Mouxiao Bian, Peiling Li +10
The rapid advancement of large language models (LLMs) has accelerated their integration into clinical decision support, particularly in prescription review. To enable systematic an…
Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications
Xiao Ye, Jacob Dineen, Zhaonan Li +11
Medical Large language models achieve strong scores on standard benchmarks; however, the transfer of those results to safe and reliable performance in clinical workflows remains a…
CC-LEARN: Cohort-based Consistency Learning
Xiao Ye, Shaswat Shrivastava, Zhaonan Li +6
Large language models excel at many tasks but still struggle with consistent, robust reasoning. We introduce Cohort-based Consistency Learning (CC-Learn), a reinforcement learning…
BOW: Training Language Models to Reason Over Plausible Next Words
Ming Shen, Zhikun Xu, Jacob Dineen +2
Next-word prediction (NWP) trains language models against a single observed continuation, even though many contexts admit multiple plausible next words. Recent RL-based next-word r…
QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA
Jacob Dineen, Aswin RRV, Qin Liu +8
Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the tra…
ToW: Thoughts of Words Improve Reasoning in Large Language Models
Zhikun Xu, Ming Shen, Jacob Dineen +6
We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects f…