- Generative artificial intelligence tools are powered by large language models trained on general-purpose datasets.
- These tools are susceptible to hallucinations, which is a concern for government agencies involved in critical missions.
- To reduce hallucinations, data science teams can follow a 6-step process to fine-tune the models with domain-specific data.
- The process involves selecting relevant datasets, preparing data, initializing the model, setting up evaluation functions, fine-tuning through training, and optimizing performance.
- Although resource-intensive, fine-tuning can result in cost reduction compared to training models from scratch.
- General Dynamics Information Technology offers resources to help government agencies fine-tune their models.
- GDIT and partners are developing capabilities, including real-time detection and correction of AI hallucinations, through Luna AI Digital Accelerator.
考察：政府機関が重要なミッションに関与する際には、ハラシネーションが懸念されるため、ジェネラティブ人工知能ツールのドメイン固有データを用いたファインチューニングが重要であると指摘されている。リソースは必要だが、ファインチューニングはコスト削減につながる可能性がある。また、General Dynamics Information Technologyは政府機関がモデルを調整するのにリソースを提供しており、Luna AI Digital Acceleratorを通じてAI alucinationsのリアルタイム検出および修正の能力を開発している。
元記事: https://executivebiz.com/2024/10/gdits-dave-vennergrund-on-reducing-ai-hallucinations/