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A Guide to 400+ Categorized Large Language Model(LLM) Datasets

Analytics Vidhya

And to top it off, this collection […] The post A Guide to 400+ Categorized Large Language Model(LLM) Datasets appeared first on Analytics Vidhya.

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Microsoft Researchers Introduce Advanced Query Categorization System to Enhance Large Language Model Accuracy and Reduce Hallucinations in Specialized Fields

Marktechpost

Large language models (LLMs) have revolutionized the field of AI with their ability to generate human-like text and perform complex reasoning. When trained on large datasets, these models often miss critical information from specialized domains, leading to hallucinations or inaccurate responses.

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Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

Marktechpost

Large Language Models (LLMs) signify a revolutionary leap in numerous application domains, facilitating impressive accomplishments in diverse tasks. With billions of parameters, these models demand extensive computational resources for operation. Yet, their immense size incurs substantial computational expenses.

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The Vulnerabilities and Security Threats Facing Large Language Models

Unite.AI

Large language models (LLMs) like GPT-4, DALL-E have captivated the public imagination and demonstrated immense potential across a variety of applications. Question answering: They can provide informative answers to natural language questions across a wide range of topics.

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Microsoft Researchers Combine Small and Large Language Models for Faster, More Accurate Hallucination Detection

Marktechpost

Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, they face a significant challenge: hallucinations, where the models generate responses that are not grounded in the source material. 1% across all datasets after filtering.

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Training Improved Text Embeddings with Large Language Models

Unite.AI

They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more. vector embedding Recent advances in large language models (LLMs) like GPT-3 have shown impressive capabilities in few-shot learning and natural language generation.

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Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review

Marktechpost

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Provide a thorough investigation of the potential of graph structures to address the limitations of LLMs.