Tutorials on Natural Language Processing

Learn about Natural Language Processing from fellow newline community members!

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  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
NEW

Fine-Tuning LLMs for Customer Support

Learn how fine-tuning LLMs for customer support can enhance response accuracy, efficiency, and brand alignment through tailored training methods.
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How LLMs Negotiate Roles in Multi-Agent Systems

Explore how Large Language Models enhance role negotiation in multi-agent systems, improving efficiency and adaptability through advanced communication.

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NEW

How to Preprocess Data for Multilingual Fine-Tuning

Learn essential preprocessing steps for multilingual data to enhance fine-tuning of language models and ensure quality, diversity, and compliance.
NEW

Retrieval-Augmented Generation for Multi-Turn Prompts

Explore how Retrieval-Augmented Generation enhances multi-turn conversations by integrating real-time data for accurate and personalized responses.
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Stemming vs Lemmatization: Impact on LLMs

Explore the differences between stemming and lemmatization in LLMs, their impacts on efficiency vs. accuracy, and optimal strategies for usage.

Relative vs. Absolute Positional Embedding in Decoders

Explore the differences between absolute and relative positional embeddings in transformers, highlighting their strengths, limitations, and ideal use cases.

Annotated Transformer: LayerNorm Explained

Explore how LayerNorm stabilizes transformer training, enhances gradient flow, and improves performance in NLP tasks through effective normalization techniques.

How to Choose Embedding Models for LLMs

Choosing the right embedding model is crucial for AI applications, impacting accuracy, efficiency, and scalability. Explore key criteria and model types.

Sequential User Behavior Modeling with Transformers

Explore how transformer models enhance sequential user behavior prediction, offering improved accuracy, scalability, and applications across industries.

Top Tools for LLM Error Analysis

Explore essential tools and techniques for analyzing errors in large language models, enhancing their performance and reliability.

Optimizing Contextual Understanding in Support LLMs

Learn how to enhance customer support with LLMs through contextual understanding and optimization techniques for better accuracy and efficiency.

Real-World LLM Benchmarks: Metrics and Methods

Explore essential metrics, methods, and frameworks for evaluating large language models, addressing performance, accuracy, and environmental impact.

How to Debug Bias in Deployed Language Models

Learn how to identify and reduce bias in language models to ensure fair and accurate outputs across various demographics and industries.

Best Practices for Evaluating Fine-Tuned LLMs

Learn best practices for evaluating fine-tuned language models, including setting clear goals, choosing the right metrics, and avoiding common pitfalls.

Dynamic Context Injection with Retrieval Augmented Generation

Learn how dynamic context injection and Retrieval-Augmented Generation enhance large language models' performance and accuracy with real-time data integration.

Trade-offs in Subword Tokenization Strategies

Explore the trade-offs in subword tokenization strategies, comparing WordPiece, BPE, and Unigram to optimize AI model performance.

Common Errors in LLM Pipelines and How to Fix Them

Explore common errors in LLM pipelines, their causes, and effective solutions to enhance reliability and performance.

How Retrieval Augmented Generation Affects Scalability

Explore how Retrieval Augmented Generation (RAG) enhances scalability in AI systems by merging real-time data retrieval with large language models.

Context-Aware Prompting with LangChain

Explore context-aware prompting techniques with LangChain, enhancing AI applications through tailored data integration for improved accuracy and performance.

How to Choose Automation Frameworks for LLM Pipelines

Learn how to select the best automation frameworks for LLM pipelines, focusing on compatibility, integration, scalability, and monitoring features.

Fine-Tuning LLMs on Imbalanced Customer Support Data

Learn how fine-tuning large language models on imbalanced customer support data can enhance performance, accuracy, and customer satisfaction.

Training LLMs with Multilingual Customer Support Data

Explore the complexities of training multilingual LLMs using diverse support data, addressing challenges and strategies for effective deployment.

How To Fine-Tune Hugging Face Models on Custom Data

Learn how to fine-tune language models on custom data to enhance performance for specific tasks using Hugging Face's tools and techniques.

Bias in LLMs: Origins and Mitigation Strategies

Explore the origins of bias in large language models and effective strategies to mitigate its harmful effects in AI applications.

Fine-Tuning LLMs for Rare Event Data Generation

Explore how fine-tuning Large Language Models can enhance data generation for rare events, improving predictive accuracy and model performance.

RAG vs Fine-Tuning: Best for Customer Support

Explore the strengths of Retrieval-Augmented Generation and Fine-Tuning for customer support, and find the best fit for your needs.

Top Metrics for Evaluating LLMs in Customer Support

Learn the essential metrics for evaluating large language models in customer support to enhance accuracy, speed, and user satisfaction.

5 Steps for Debugging LLM Prompts

Learn the essential steps to debug prompts for Large Language Models, ensuring accuracy and reliability in AI outputs.

Zero-Shot vs Few-Shot Prompting: Key Differences

Explore the differences between zero-shot and few-shot prompting methods for optimizing language model performance and accuracy.

Sliding Window in RAG: Step-by-Step Guide

Learn how the sliding window technique can enhance context handling and improve accuracy in Retrieval-Augmented Generation systems.