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New Trends in LLM: Overview with Focus on xLLM
Read full article and download PowerPoint presentation at https://mltblog.com/3KqlNO7
If you ever wondered how xLLM is different from other LLM and RAG architectures, what are the foundational changes that make it appealing to fortune 100 companies, and what are the innovations being copied by competitors, read on. In this article, I share the latest trends and provide a high-level summary of xLLM, describing the ground-breaking technologies that make it unique, faster, and better for professional users and experts. In particular, I share my PowerPoint presentation on the topic.
Search is becoming hot again, this time powered by RAG and LLMs rather than PageRank. New LLMs may not use transformers, and energy-efficient implementations are gaining popularity, with an attempt to lower GPU usage, and thus costs. Yet all but xLLM still rely on Blackbox neural networks.
Great evaluation metrics remain elusive and will remain so probably forever: in the end, LLMs, just like clustering, are part of unsupervised learning. Two users looking at a non-trivial dataset will never agree on what the “true” underlying cluster structure is. Because “true” is meaningless in this context. The same applies to LLMs. With some exceptions: when used for predictive analytics, that is, supervised learning, it is possible to tell which LLM is best in absolute terms (to some extent; it also depends on the dataset).
Read full article and download PowerPoint presentation at https://mltblog.com/3KqlNO7
If you ever wondered how xLLM is different from other LLM and RAG architectures, what are the foundational changes that make it appealing to fortune 100 companies, and what are the innovations being copied by competitors, read on. In this article, I share the latest trends and provide a high-level summary of xLLM, describing the ground-breaking technologies that make it unique, faster, and better for professional users and experts. In particular, I share my PowerPoint presentation on the topic.
Search is becoming hot again, this time powered by RAG and LLMs rather than PageRank. New LLMs may not use transformers, and energy-efficient implementations are gaining popularity, with an attempt to lower GPU usage, and thus costs. Yet all but xLLM still rely on Blackbox neural networks.
Great evaluation metrics remain elusive and will remain so probably forever: in the end, LLMs, just like clustering, are part of unsupervised learning. Two users looking at a non-trivial dataset will never agree on what the “true” underlying cluster structure is. Because “true” is meaningless in this context. The same applies to LLMs. With some exceptions: when used for predictive analytics, that is, supervised learning, it is possible to tell which LLM is best in absolute terms (to some extent; it also depends on the dataset).