Remove Conversational AI Remove Large Language Models Remove Natural Language Processing
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Top 10 Large Language Models on Hugging Face

Analytics Vidhya

Introduction Hugging Face has become a treasure trove for natural language processing enthusiasts and developers, offering a diverse collection of pre-trained language models that can be easily integrated into various applications.

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Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing

Marktechpost

Large language models (LLMs) have shown exceptional capabilities in understanding and generating human language, making substantial contributions to applications such as conversational AI. Chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services.

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Conversational AI use cases for enterprises

IBM Journey to AI blog

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. This sophisticated foundation propels conversational AI from a futuristic concept to a practical solution. billion by 2030.

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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. Thanks to the widespread adoption of ChatGPT, millions of people are now using Conversational AI tools in their daily lives.

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Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

Flipboard

Hallucinations in large language models (LLMs) refer to the phenomenon where the LLM generates an output that is plausible but factually incorrect or made-up. His area of research is all things natural language (like NLP, NLU, and NLG). About the Authors Shayan Ray is an Applied Scientist at Amazon Web Services.

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RAGate: Adaptive RAG for Conversational AI

Towards AI

Last Updated on November 10, 2024 by Editorial Team Author(s): Rupali Patil Originally published on Towards AI. Building Conversational AI systems is hard!!! The very popular RAG (Retrieval-Augmented Generation) has revolutionized conversational AI by seamlessly integrating external knowledge with LLM’s internal knowledge.

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This AI Paper Unveils the Potential of Speculative Decoding for Faster Large Language Model Inference: A Comprehensive Analysis

Marktechpost

Large Language Models (LLMs) are crucial to maximizing efficiency in natural language processing. These models, central to various applications ranging from language translation to conversational AI, face a critical challenge in the form of inference latency.