# Can Large Language Models assist in Hazard Analysis?

arXiv: https://arxiv.org/abs/2303.15473 · HTML: https://mlsanity.com/paper/2303.15473/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [ChatGPT: The End of Online Exam Integrity?](https://mlsanity.com/paper/2212.09292/) (2022)
- [ChatGPT is not all you need. A State of the Art Review of large Generative AI models](https://mlsanity.com/paper/2301.04655/) (2023)
- [SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities](https://mlsanity.com/paper/2207.05643/) (2022)

## Built on by (most cited first, 8 of 8)

- [Engineering Safety Requirements for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2403.16289/) (2024)
- [Concept-Guided LLM Agents for Human-AI Safety Codesign](https://mlsanity.com/paper/2404.15317/) (2024)
- [CoDefeater: Using LLMs To Find Defeaters in Assurance Cases](https://mlsanity.com/paper/2407.13717/) (2024)
- [Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities](https://mlsanity.com/paper/2406.05496/) (2024)
- [An LLM-Integrated Framework for Completion, Management, and Tracing of STPA](https://mlsanity.com/paper/2503.12043/) (2025)
- [The Competence Shadow: Theory and Bounds of AI Assistance in Safety Engineering](https://mlsanity.com/paper/2603.25197/) (2026)
- [Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA](https://mlsanity.com/paper/2607.08054/) (2026)
- [From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices](https://mlsanity.com/paper/2608.12025/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable natural language processing and generation capabilities and have been applied to a variety tasks, such as source code generation. This paper explores the potential of integrating LLMs in the hazard analysis for safety-critical systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a human analyst interacts with an LLM via a context-aware chat session and uses the responses to support elicitation of possible hazard causes. In this experiment, we explore CoHA with three increasingly complex versions of a simple system, using Open AI's ChatGPT service. The quality of ChatGPT's responses were systematically assessed to determine the feasibility of CoHA given the current state of LLM technology. The results suggest that LLMs may be useful for supporting human analysts performing hazard analysis.