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DeepNewz, mobile.
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The best use cases for LLMs are idea generation and brainstorming, which align perfectly with the nature of these transformer-based language models. https://t.co/6hgK4EPOUx
Challenges Effectiveness of Commercial Fine-Tuning APIs in Large Language Models Fine-tuning large language models #LLMs to incorporate new information and update existing knowledge has become a significant focus. Current commercial fine-tuning APIs from providers like… https://t.co/HsLddPqZiQ
Explore Ethical Considerations of LLMs. 🤖⚖️ Address bias, privacy, and responsible AI use as large language models advance. #Ethics #LLMs #ArtificialIntelligence #Aibrilliance. Learn more at https://t.co/GcG9HYtbLz. https://t.co/yPnbpSV9l9
A new evaluation framework called FineTuneBench has been introduced to assess the effectiveness of commercial fine-tuning APIs in updating large language models (LLMs) with new knowledge. The study reveals substantial limitations in these APIs, including those used for popular models like ChatGPT and Gemini, generally failing to infuse new information effectively. Among the tested APIs, Finetune 4o-mini was found to be the most effective, while Gemini showed less capability in updating its knowledge base. The findings highlight substantial limitations in current commercial fine-tuning APIs, raising questions about their ability to enhance LLMs' performance.
