Hallucination Mitigation Task

A Hallucination Mitigation Task is a model improvement task that reduces language model hallucinations through technical interventions, training modifications, or architectural changes.

AKA: Hallucination Reduction Task, Hallucination Prevention Task, Factuality Improvement Task, Truthfulness Enhancement Task.
Context:
- It can typically involve fine-tuning with factuality-focused datasets.
- It can typically implement retrieval-augmented generation for knowledge grounding.
- It can often employ reinforcement learning with truthfulness rewards.
- It can often require iterative refinement through human feedback.
- It can range from being a Pretraining Mitigation Task to being a Post-Training Mitigation Task, depending on its intervention phase.
- It can range from being a Data-Based Mitigation Task to being a Architecture-Based Mitigation Task, depending on its approach.
- It can range from being a Preventive Mitigation Task to being a Corrective Mitigation Task, depending on its timing.
- It can range from being a Domain-Specific Mitigation Task to being a General Mitigation Task, depending on its scope.
- ...
Examples:
- Training-Based Mitigation, such as:
  - Factuality Fine-Tuning with verified data.
  - Contrastive Learning Mitigation using positive-negative pairs.
  - Adversarial Training Mitigation against hallucination examples.
- Architecture-Based Mitigation, such as:
  - Retrieval Integration Mitigation adding knowledge retrieval.
  - Attention Modification Mitigation improving context focus.
  - Ensemble Mitigation combining multiple models.
- ...
Counter-Examples:
- Hallucination Detection Task, which identifies rather than prevents.
- Post-Hoc Correction Task, which fixes after generation.
- Performance Optimization Task, which may increase hallucination.
See: Language Model Hallucination, Hallucination Detection Task, Socio-Technical Hallucination Mitigation Framework, Retrieval-Augmented Generation, Factual Grounding Task, Model Fine-Tuning, AI Safety Task.