Selective Risk-Aware Mental-Health Response Refinement
This repository releases two Gemma-4-E4B-it PEFT LoRA adapters for safety-aware response refinement in single-turn mental-health question answering.
Released Adapters
- Risk-tuned denoiser (
/): repairs residual safety and clinical-boundary risks while preserving response quality and specificity.
- SFT refiner (
/sft-refiner): rewrites unsafe drafts into supportive, specific, and professionally bounded responses.
Both adapters use LoRA rank 8 over the language-model self-attention Q/K/V/O projections.
Method
The system first generates a refined response using supervised fine-tuning. A selectively invoked risk-aware denoiser then repairs remaining violations using aspect-guided corruption, span-level risk-aware masking, and risk-weighted token-level training.
Base Model
google/gemma-4-E4B-it
Code
Intended Use
This model is intended for research on mental-health response refinement, safety-aware generation, and parameter-efficient fine-tuning.
Generated responses may contain inaccurate or unsafe information. The model requires human review and appropriate safeguards in mental-health applications. It should not be used for diagnosis, treatment, medication guidance, crisis assessment, or emergency intervention.
