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Beyond Search Engines: The Rise of LLM-Powered Web Browsing Agents

Unite.AI

In recent years, Natural Language Processing (NLP) has undergone a pivotal shift with the emergence of Large Language Models (LLMs) like OpenAI's GPT-3 and Google’s BERT. Using their extensive training data, LLM-based agents deeply understand language patterns, information, and contextual nuances.

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The Top 8 Computing Stories of 2024

Flipboard

The ever-growing presence of artificial intelligence also made itself known in the computing world, by introducing an LLM-powered Internet search tool, finding ways around AIs voracious data appetite in scientific applications, and shifting from coding copilots to fully autonomous coderssomething thats still a work in progress. Perplexity.ai

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Agent Memory in AI: How Persistent Memory Could Redefine LLM Applications

Unite.AI

Large language models (LLMs) , such as GPT-4 , BERT , Llama , etc., Simple rule-based chatbots, for example, could only provide predefined answers and could not learn or adapt. In customer support, for instance, AI-powered chatbots can store and retrieve user-specific details like purchase histories or previous complaints.

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LLM-as-judge for enterprises: evaluate model alignment at scale

Snorkel AI

LLM-as-Judge has emerged as a powerful tool for evaluating and validating the outputs of generative models. LLMs (and, therefore, LLM judges) inherit biases from their training data. In this article, well explore how enterprises can leverage LLM-as-Judge effectively , overcome its limitations, and implement best practices.

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The Full Story of Large Language Models and RLHF

AssemblyAI

This is heavily due to the popularization (and commercialization) of a new generation of general purpose conversational chatbots that took off at the end of 2022, with the release of ChatGPT to the public. But, how to determine how much data one needs to train an LLM? When training a model, its size is only one side of the picture.

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Speculative Decoding for LLM

Bugra Akyildiz

Speculative decoding applies the principle of speculative execution to LLM inference. The process involves two main components: A smaller, faster "draft" model The larger target LLM The draft model generates multiple tokens in parallel, which are then verified by the target model.

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An Introduction to Large Language Models (LLMs)

Analytics Vidhya

LLMs can perform many types of language tasks, such as translating languages, analyzing sentiments, chatbot […] The post An Introduction to Large Language Models (LLMs) appeared first on Analytics Vidhya. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language.