Remove Large Language Models Remove Linked Data Remove LLM
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Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

John Snow Labs

Clinical bias in LLM (Language Learning Models) refers to the unfair or unequal representation or treatment based on medical or clinical information. To test this, we fed the patient_info_A with the diagnosis to the Language Model (LLM) and requested a treatment plan. Supported Tasks: [link] 2.

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Anthony Deighton, CEO of Tamr – Interview Series

Unite.AI

Under the academic leadership of Turing Award winner Michael Stonebraker, the question the team were investigating was “can we link data records across hundreds of thousands of sources and millions of records.” This input, of course, goes to refine and update the models. Fundamentally, LLMs are about language.

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An introduction to preparing your own dataset for LLM training

AWS Machine Learning Blog

Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of linguistic tasks. However, the performance of these models is heavily influenced by the data used during the training process. models using torchtune on Amazon SageMaker This post is co-written with Metas PyTorch team.

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Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

In the context of enterprise data asset search powered by a metadata catalog hosted on services such Amazon DataZone, AWS Glue, and other third-party catalogs, knowledge graphs can help integrate this linked data and also enable a scalable search paradigm that integrates metadata that evolves over time. account } WHERE { ?asset

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Token Auction Model

Bugra Akyildiz

The integration of large language models (LLMs) into economic mechanisms represents a paradigm shift in how multi-agent systems collaborate to generate content. Google Research published a blog post on this through token auction model. At its core, the model treats each token (e.g.,

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