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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2511.10027 |
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| _version_ | 1866912710022660096 |
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| author | Surana, Risha Ye, Qinyuan Swayamdipta, Swabha |
| author_facet | Surana, Risha Ye, Qinyuan Swayamdipta, Swabha |
| contents | Emergency responders managing hazardous material HAZMAT incidents face critical, time-sensitive decisions, manually navigating extensive chemical guidelines. We investigate whether today's language models can assist responders by rapidly and reliably understanding critical information, identifying hazards, and providing recommendations. We introduce the Chemical Emergency Response Evaluation Framework (ChEmREF), a new benchmark comprising questions on 1,035 HAZMAT chemicals from the Emergency Response Guidebook and the PubChem Database. ChEmREF is organized into three tasks: (1) translation of chemical representation between structured and unstructured forms (e.g., converting C2H6O to ethanol), (2) emergency response generation (e.g., recommending appropriate evacuation distances) and (3) domain knowledge question answering from chemical safety and certification exams. Our best evaluated models received an exact match of 68.0% on unstructured HAZMAT chemical representation translation, a LLM Judge score of 52.7% on incident response recommendations, and a multiple-choice accuracy of 63.9% on HAMZAT examinations. These findings suggest that while language models show potential to assist emergency responders in various tasks, they require careful human oversight due to their current limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChEmREF: Evaluating Language Model Readiness for Chemical Emergency Response Surana, Risha Ye, Qinyuan Swayamdipta, Swabha Artificial Intelligence Emergency responders managing hazardous material HAZMAT incidents face critical, time-sensitive decisions, manually navigating extensive chemical guidelines. We investigate whether today's language models can assist responders by rapidly and reliably understanding critical information, identifying hazards, and providing recommendations. We introduce the Chemical Emergency Response Evaluation Framework (ChEmREF), a new benchmark comprising questions on 1,035 HAZMAT chemicals from the Emergency Response Guidebook and the PubChem Database. ChEmREF is organized into three tasks: (1) translation of chemical representation between structured and unstructured forms (e.g., converting C2H6O to ethanol), (2) emergency response generation (e.g., recommending appropriate evacuation distances) and (3) domain knowledge question answering from chemical safety and certification exams. Our best evaluated models received an exact match of 68.0% on unstructured HAZMAT chemical representation translation, a LLM Judge score of 52.7% on incident response recommendations, and a multiple-choice accuracy of 63.9% on HAMZAT examinations. These findings suggest that while language models show potential to assist emergency responders in various tasks, they require careful human oversight due to their current limitations. |
| title | ChEmREF: Evaluating Language Model Readiness for Chemical Emergency Response |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.10027 |