2 citations · 2 across the 6 of their papers we have counts for
6 papers
TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
Erica Zhang, Fangzhao Zhang, Aneesh Pappu +5
Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation. It is also a canonical testbed for agentic languag…
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
Siyu Chen, Miao Lu, Beining Wu +7
Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend…
Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
Yikai Wang, Shang Liu, Jose Blanchet
Reinforcement learning from human feedback (RLHF) is a central post-training tool for aligning large language models, but its training reward is only a learned proxy for true human…
When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions
Lezhi Tan, Naomi Sagan, Lihua Lei +1
AI systems increasingly assist decision making by producing cheap factor-level assessments of complex inputs, but these assessments can be biased or incomplete. We study selective…
Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration
Erica Zhang, Naomi Sagan, Danny Tse +3
Large language models (LLMs) encode rich semantic knowledge that can be useful for supervised learning, but their outputs are unreliable as statistical priors: they may be noisy, m…
LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization
Erica Zhang, Ryunosuke Goto, Naomi Sagan +7
We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. Unlike traditional methods that rely…