AIM-Intelligence/COMPASS_gemma-3-4b-it_LoRA

COMPASS Gemma-3-4B-it LoRA (Policy-aware LODO SFT)

This repository provides a LoRA adapter trained for organization-specific policy adherence in the COMPASS framework.

Training Data

Policy-aware SFT dataset built from COMPASS scenarios:

• —Setup: Leave-One-Domain-Out (LODO)
• —Held-out domain: TelePath (Telecom)
• —Train domains (7): AutoViaMotors, CityGov, FinSecure, MediCarePlus, PlanMyTrip, TutoraVerse, VirtuRecruit
• —Training size: 4,121 query–response pairs

Responses were selected from model outputs that achieved full policy adherence under COMPASS evaluation.

Training Configuration

• —Method: LoRA adapters
• —Epochs: 3
• —LoRA rank (r): 32
• —LoRA alpha: 64
• —Peak learning rate: 3e-4
• —Optimizer: AdamW
• —Batch size: 32
• —LR schedule: cosine
• —Quantization: 8-bit during training

Evaluation (Held-out TelePath Domain)

Policy Alignment Score (PAS) breakdown on TelePath:

Note: Fine-tuning may trade off some “Allowed Base” performance while improving denied-query handling.

Citation

@misc{choi2026compass,
      title={COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs}, 
      author={Dasol Choi and DongGeon Lee and Brigitta Jesica Kartono and Helena Berndt and Taeyoun Kwon and Joonwon Jang and Haon Park and Hwanjo Yu and Minsuk Kahng},
      year={2026},
      eprint={2601.01836},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.01836}, 
}