Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment
Faizan Iqbal
Why It Matters
What makes this one worth your time
Understanding the limitations of language models in multi-sensor environments is crucial for their deployment in safety-critical systems.
The study highlights the limitations of large language models in multi-sensor hazard assessment.
Summary
The paper benchmarks five large language models on their ability to assess multi-sensor physical hazard data across 60 scenarios, revealing that these models struggle with multi-sensor scenarios but perform well on single-sensor threshold violations.
Key contributions
- Benchmarking large language models on multi-sensor hazard assessment.
- Empirical evaluation of model performance across different data formats.
Notable insights
- Models perform better with single-sensor data than with multi-sensor data.
- Prose formatting may enhance model performance over structured tabular data.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios across three categories - multi-sensor joint assessment, response proportionality, and pattern disambiguation - with 1,800 API calls at temperature 0.0, we find that all tested models consistently produced no precautionary warning signal across the tested scenarios where multiple sensors are simultaneously elevated below their individual safety limits, while achieving near-perfect accuracy on single-sensor threshold violations. All five models (ChatGPT-4o, Gemini 2.5 Flash, DeepSeek, Kimi, Llama 3.1 8B) score near zero on Category A multi-sensor scenarios (Q2: 0.000-0.208; Q3: 0.000-0.592) compared to strong performance on single-sensor scenarios (Category B Q1: 0.975-1.000). Structured tabular formatting shows no consistent advantage over plain prose; ChatGPT-4o performs significantly better under prose (p = 0.001). These findings have direct implications for practitioners deploying the tested models in physical safety monitoring systems.