Papers by Jinxuan Zhang

2 papers
A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)

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Challenge: Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses.
Approach: They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks .
Outcome: The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem .
Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)

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Challenge: Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences.
Approach: They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones.
Outcome: The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost.

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