Overview
This project studies whether a small LLM can be trained to express uncertainty instead of confidently hallucinating. The model is fine-tuned to prefer safe abstention when it lacks reliable knowledge.
Key Highlights
- Fine-tunes Llama 3.2 1B Instruct on an uncertainty-focused instruction dataset.
- Demonstrates a qualitative reduction in hallucinated answers on unknown questions.
- Shows an important trade-off: over-abstention, where the model may say “I don’t know” even for answerable prompts.
- Includes links to both the model and dataset on Hugging Face.
Technologies
- Core: Python, PyTorch, Hugging Face ecosystem
- Training Setup: Google Colab (T4 GPU)
- Approach: Instruction tuning focused on uncertainty-aware responses