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Adversarial Databases Improve Success in Retrieval-based Large Language Models

Autor: Wu, Sean, Koo, Michael, Kao, Li Yo, Black, Andy, Blum, Lesley, Scalzo, Fabien, Kurtz, Ira

Open-source LLMs have shown great potential as fine-tuned chatbots, and demonstrate robust abilities in reasoning and surpass many existing benchmarks. Retrieval-Augmented Generation (RAG) is a technique for improving the performance of LLMs on tasks

Externí odkaz: http://arxiv.org/abs/2407.14609

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A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology

Autor: Wu, Sean, Koo, Michael, Blum, Lesley, Black, Andy, Kao, Liyo, Scalzo, Fabien, Kurtz, Ira

In recent years, there have been significant breakthroughs in the field of natural language processing, particularly with the development of large language models (LLMs). These LLMs have showcased remarkable capabilities on various benchmarks. In the

Externí odkaz: http://arxiv.org/abs/2308.04709

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