Traditional Chinese Cybersecurity Dialogue Dataset for SFT Training

Real cybersecurity dialogue records from the Langfuse platform, processed for traditional Chinese reasoning SFT training. The data underwent deduplication, answer regeneration via the Gemini model, and thought process rewriting via the Qwen model.

Use Cases

• Fine-tune a language model for traditional Chinese cybersecurity question-answering using the processed dialogue format.
• Train a model to generate assistant responses in the cybersecurity domain based on user and system messages.
• Analyze the structure and patterns of real cybersecurity dialogues that include a 'think' label for reasoning.
• Study the application of model-rewritten thought processes (Rethink) for improving reasoning in security-focused text generation.

Strengths

• Based on real dialogue records from a cybersecurity platform, providing authentic domain context.
• Processed through multiple AI models (Gemini, Qwen) for answer regeneration and thought process refinement.
• Formatted specifically for traditional Chinese reasoning SFT training with a 'think' label.

Limitations

• The sample size and total row count are unknown, making scale assessment difficult.
• Specific columns and data schema are not provided, limiting understanding of the data structure.
• Potential for model-introduced bias or noise from the regeneration and rewriting steps.

Provenance

Source

Langfuse platform (litellm) from a cybersecurity agency.

Collection Method

Real conversation logs (system + user + assistant) processed via deduplication, model regeneration, and translation.

Freshness

Last updated on 2026-03-07.