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Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

John Snow Labs

Supported Data: [link] data Testing in 3 lines of Code !pip report() The report provides a comprehensive overview of our test outcomes using the Medical-files data, which comprises 49 entries. In Conclusion: Setting up the Harness is like preparing a toolbox for a job.

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A New Study from the University of Wisconsin Investigates How Small Transformers Trained from Random Initialization can Efficiently Learn Arithmetic Operations Using the Next Token Prediction Objective

Marktechpost

For various downstream tasks, including language and code translation, compositional thinking, and fundamental arithmetic operations, large language models like GPT-3/4, PaLM, and LaMDA have shown general-purpose features, sometimes emergent skills. Data on the flow of cognition throughout training.

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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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OpenAI announces ChatGPT

Bugra Akyildiz

I am sure this is relatively cherry-picked example, but it shows the training methodology and reinforcement learning’s success on a very large language model well. It captures and provides the timings for all the layers present in the model.

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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.

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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.

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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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