most citedResponsible AI in Construction Safety: Systematic Evaluation of Large Language Models and Prompt Engineering

2 citations · 2 across the 3 of their papers we have counts for

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cs.AI2026

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

Xiangning Lin, Shenzhe Zhu, Shu Yang +23

System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are r…

cs.AI2026

Interactive Task Alignment as a POMDP

Andy Dai, Zexue He, Zhenyu Zhang +2

Current benchmarks for language models primarily evaluate execution on fully specified tasks. However, real user tasks are often ambiguous. Users arrive with incomplete, explorator…

cs.AI2026

Interactive Evaluation Requires a Design Science

Keyang Xuan, Peiyang Song, Pan Lu +10

AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other…

cs.AI20259 cited

Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety

Yuxin Zhang, Xi Wang, Mo Hu +1

Information retrieval and question answering from safety regulations are essential for automated construction compliance checking but are hindered by the linguistic and structural…

cs.AI20242 cited

Responsible AI in Construction Safety: Systematic Evaluation of Large Language Models and Prompt Engineering

Farouq Sammour, Jia Xu, Xi Wang +2

Construction remains one of the most hazardous sectors. Recent advancements in AI, particularly Large Language Models (LLMs), offer promising opportunities for enhancing workplace…