SuicideWatch: A Clpsych-2024 Shared Task
Situation: Posts on r/SuicideWatch often contain severe self-harm or suicidal ideation. Existing safeguard systems can flag high-risk posts but provide little transparency about which parts of the text triggered the alert.
Task: Build a privacy-preserving, interpretable system that can highlight the specific text segments that correspond to suicidal ideation risk categories.
Action: Deployed a locally hosted, quantized 70B LLM model to ensure data privacy and efficient inference. Used prompt engineering to align the model with a psychological suicidal-ideation coding framework, and integrated the workflow using LangChain to extract fine-grained text segments that match the flagged risk signals.
Result: The system produced the most precise and interpretable risk explanations in the evaluation, outperforming all competing participants and achieving the top ranking.
Tech stack: LLM(LLaMA), quantization, LangChain.