Credal Large Language Models for Semantic Commitment under Uncertainty
This research introduces Credal Large Language Models (CLLMs) to handle uncertainty by representing it as a 'credal set' instead of a single prediction. By using an ensemble of LoRA adapters, the model exposes the spread of plausible answers. This allows for new commitment scores that translate this uncertainty into actionable decisions, improving accuracy in reasoning and hallucination detection.