AI News

JUNE 14, 2024

Researchers have introduced a novel approach called natural language embedded programs (NLEPs) to improve the numerical and symbolic reasoning capabilities of large language models (LLMs). Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Marktechpost

JULY 15, 2023

The advent of large language models (LLMs) has sparked significant interest among the public, particularly with the emergence of ChatGPT. These models, which are trained on extensive amounts of data, can learn in context, even with minimal examples.

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Viso.ai

JULY 18, 2024

With the advent of platforms like ChatGPT, these terms have now become a word of mouth for everyone. What are Large Language Models (LLMs)? An easy way to describe LLM is an AI algorithm capable of understanding and generating human language. Introduced by Vaswani et al.

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Topbots

JANUARY 30, 2023

400k AI-related online texts since 2021) Disclaimer: This article was written without the support of ChatGPT. In the last couple of years, Large Language Models (LLMs) such as ChatGPT, T5 and LaMDA have developed amazing skills to produce human language. Association for Computational Linguistics. [2]

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ODSC - Open Data Science

APRIL 3, 2023

Do you yearn to compare different QA models but dread the time-consuming process of setting them up? Or do you want to compare the capabilities of ChatGPT against regular fine-tuned QA models? Lastly, we are currently working on integrating recent works on Large Language Models such as ChatGPT.

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Marek Rei

NOVEMBER 4, 2024

It is probably good to also to mention that I wrote all of these summaries myself and they are not generated by any language models. Are Emergent Abilities of Large Language Models a Mirage? Do Large Language Models Latently Perform Multi-Hop Reasoning? Here we go. NeurIPS 2023. ArXiv 2024.

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NYU Center for Data Science

JANUARY 24, 2025

Rudner Receives Major Grant to Study Uncertainty Quantification in Large LanguageModels Large language models (LLMs) often express high confidence even when providing incorrect answers. CDS Faculty Member Tim G. This fundamental challenge in AI reliability motivated CDS faculty member Tim G.

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