Causalair: Aviation Accident Narratives with Reasoning and Cause Labels

A dataset for fine-tuning large language models on aviation accident analysis. It contains fields for accident narratives, reasoning processes, and accident causes, and is structured for both Supervised Fine-Tuning and Direct Preference Optimization stages. The dataset was uploaded by jifei0126 to Hugging Face and was last updated on January 11, 2026.

Use Cases

• Fine-tuning LLMs for causal reasoning based on aviation accident narratives.
• Training models to generate structured accident cause reports based on narrative descriptions.
• Developing AI assistants for aviation safety analysis based on the provided reasoning process field.
• Benchmarking LLM performance on specialized, safety-critical text generation tasks.

Strengths

• Explicitly structured for two-stage LLM fine-tuning (SFT and DPO).
• Contains three distinct text fields (narrative, reasoning, cause) for each data point.

Limitations

• Row count, file formats, and column-level documentation are unknown.
• The dataset's source, temporal coverage, and geographic scope are unspecified.
• License information is unavailable, which may restrict usage.

Provenance

Source

huggingface

Freshness

Last updated 2026-01-11 15:01:59.