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2026-10-21, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20250223 Question.1
Questioner: haun620@kyonggi.ac.kr 2025-02-17 오후 12:50:41 |
ICACT20250223 Answer.1
Answer by Auhor alihussainnrana@gmail.com 2025-02-17 오후 12:50:41
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| What do you think are the solutions to the Foundation model's data dependencies and data limitations? |
One solution to the Foundation model's data dependencies and limitations is improving data quality through better curation, filtering, and augmentation techniques. Synthetic data generation can help overcome data scarcity while maintaining diversity and reducing biases. Additionally, integrating domain-specific fine-tuning with smaller, high-quality datasets can enhance performance without excessive data reliance. Lastly, leveraging federated learning and privacy-preserving techniques allows models to learn from distributed datasets without centralizing sensitive information. |
ICACT20250223 Question.2
Questioner: taikung@gmail.com 2025-02-19 오후 8:06:21 |
ICACT20250223 Answer.2
Answer by Auhor alihussainnrana@gmail.com 2025-02-19 오후 8:06:21
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| Hello,
You mention the interpretability gap in foundational models. Could you please tell me current approaches to dealing with this? |
The interpretability gap in foundational models is addressed through several approaches. Explainable AI (XAI) techniques, such as SHAP and LIME, help elucidate model decisions by highlighting influential features. Model-agnostic methods like counterfactual explanations illustrate how changes in input affect outputs. Additionally, integrating human-in-the-loop systems allows experts to validate and refine model interpretations, enhancing transparency and trust. Ongoing research focuses on developing more intuitive and accessible interpretation tools to bridge this gap effectively. |
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