8 papers · 1 filter
Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems
Ming Shen, Chao Shang, Sadat Shahriar +4
Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit social expectations: age…
Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation
Minghe Shen, Ananth Balashankar, Adam Fisch +2
The ability to rigorously estimate the failure rates of large language models (LLMs) is a prerequisite for their safe deployment. Currently, however, practitioners often face a tra…
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…