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Since OpenAI unveiled ChatGPT in late 2022, the role of foundational largelanguagemodels (LLMs) has become increasingly prominent in artificial intelligence (AI), particularly in natural language processing (NLP).
As AI continues to evolve, there is growing demand for lightweight largelanguagemodels that balance efficiency and performance. Together in this blog, were going to explore what makes an LLM lightweight, the top models in 2025, and how to choose the right one for yourneeds.
One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Reasons for using RAG are clear: largelanguagemodels (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. Do LLMs Really Adapt to Domains?
LargeLanguageModels (LLMs) rely on reinforcement learning techniques to enhance response generation capabilities. One critical aspect of their development is reward modeling, which helps in training models to align better with human expectations.
Here, we look at how you can analyze a global AIresearch community using Gephi and ChatGPT. 5 Practical Business Use Cases for LargeLanguageModels LLMs are everywhere now. Let’s take a look at a few practical use cases for largelanguagemodels and how they can shape your AI endeavors too.
In a recent episode of ODSC’s Ai X Podcast , which was recorded live during ODSC West 2024 , Gary Marcus, an influential AIresearcher, shared a critical perspective on the limitations of largelanguagemodels (LLMs), emphasizing the need for true reasoning capabilities in AI.
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