most citedUProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

1 citations · 1 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CL2026

EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce

Kaiyan Zhao, Zijie Meng, Zheyong Xie +4

Large Language Model (LLM)-based agents are increasingly deployed in e-commerce applications to assist customer services in tasks such as product inquiries, recommendations, and or…

cs.CR2025

The Imitation Game: Using Large Language Models as Chatbots to Combat Chat-Based Cybercrimes

Yifan Yao, Baojuan Wang, Jinhao Duan +4

Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on trust-building, deception, and psycho…

eess.IV2025

Conformal Lesion Segmentation for 3D Medical Images

Binyu Tan, Zhiyuan Wang, Jinhao Duan +4

Medical image segmentation serves as a critical component of precision medicine, enabling accurate localization and delineation of pathological regions, such as lesions. However, e…

cs.CL2025

Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs

Zhuoxuan Zhang, Jinhao Duan, Edward Kim +1

Ambiguity is pervasive in real-world questions, yet large language models (LLMs) often respond with confident answers rather than seeking clarification. In this work, we show that…

cs.CL2025

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Zhiyuan Wang, Jinhao Duan, Qingni Wang +4

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…

cs.CL20251 cited

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

Jinhao Duan, James Diffenderfer, Sandeep Madireddy +3

As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM d…