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Comparing LLMs for Text Summarization and Question Answering

Analytics Vidhya

Large Language Models like BERT, T5, BART, and DistilBERT are powerful tools in natural language processing where each is designed with unique strengths for specific tasks. These models vary in their architecture, performance, and efficiency.

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The Full Story of Large Language Models and RLHF

AssemblyAI

In the grand tapestry of modern artificial intelligence, how do we ensure that the threads we weave when designing powerful AI systems align with the intricate patterns of human values? This question lies at the heart of AI alignment , a field that seeks to harmonize the actions of AI systems with our own goals and interests.

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Beyond Words: Unleashing the Power of Large Language Models

Analytics Vidhya

Introduction In the realm of artificial intelligence, a transformative force has emerged, capturing the imaginations of researchers, developers, and enthusiasts alike: large language models.

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Exploring the Use of LLMs and BERT for Language Tasks

Analytics Vidhya

Introduction In the rapidly evolving landscape of artificial intelligence, especially in NLP, large language models (LLMs) have swiftly transformed interactions with technology. This article explores […] The post Exploring the Use of LLMs and BERT for Language Tasks appeared first on Analytics Vidhya.

BERT 271
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Beginners’ Guide to Finetuning Large Language Models (LLMs)

Analytics Vidhya

Introduction Embark on a journey through the evolution of artificial intelligence and the astounding strides made in Natural Language Processing (NLP). In a mere blink, AI has surged, shaping our world. The seismic impact of finetuning large language models has utterly transformed NLP, revolutionizing our technological interactions.

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How to Fine-Tune Any Large Language Model (LLM)

Towards AI

Last Updated on January 29, 2025 by Editorial Team Author(s): Pranjal Khadka Originally published on Towards AI. Fine-tuning large language models (LLMs) has become an easier task today thanks to the availability of low-code/no-code tools that allow you to simply upload your data, select a base model and obtain a fine-tuned model.

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LogLLM: Leveraging Large Language Models for Enhanced Log-Based Anomaly Detection

Marktechpost

Traditional techniques include reconstruction-based methods (such as autoencoders and GANs), which rely on training models to reconstruct normal log sequences and detect anomalies based on reconstruction errors. It leverages BERT to extract semantic vectors and uses Llama, a transformer decoder, for log sequence classification.