Benchmarking AI: Finding the Best Code Generation Model using CodeBleu

Discover the future of AI code development in this comprehensive look at code generation models! Richard Walker from Lucidate delves into the exciting world of Large Language Models (LLMs) like GPT-4 and how they're shaping our coding landscape. From examining coding communities' contributions to exploring advanced fine-tuning on platforms like HuggingFace and Ollama, this video is your ultimate guide to understanding AI-powered code synthesis.
In this episode, we tackle the pivotal question: Which AI model writes code best? Unveiling the power of CodeBLEU, we reveal how it revolutionizes code evaluation, transcending beyond traditional benchmarks. Plus, get exclusive insights into constructing custom benchmarks tailored to your unique coding needs.
🔍 What you'll learn:
How LLMs leverage coding communities for better code generation.
The role of HuggingFace leaderboards in model comparison.
Custom benchmarking: your secret weapon in AI evaluation.
CodeBLEU: The metric that's changing the game in AI code synthesis.
Link to benchmarks for summarisation, translation and generation: • Text Summarisation Sho...
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#AILLM #CodeGeneration #CodeBLEU #Programming #MachineLearning #Lucidate
📧 For business inquiries: [e-mail]
#AILLM #CodeGeneration #CodeBLEU #Programming #MachineLearning #Lucidate
Other titles for this video - which do you think is best?
"AI in Action: How Does Code Generation Stack Up?"
"Exploring AI's Coding Power: A Deep Dive into CodeBLEU"
"The Truth Behind AI Code Generation: Benchmarks Revealed"
"Benchmarking AI: Finding the Best Code Generation Model"
"AI Writes Code: Evaluating Models with CodeBLEU"
"Lucidate’s Guide to Benchmarking AI for Code Generation"
"AI Code Wizards: Testing CodeBLEU on Large Language Models"
"The Developer's AI: Benchmarking Code Generation Tools"
"From StackOverflow to AI: The Code Generation Evolution"
"Unlocking AI's Potential in Code Creation: An Expert Breakdown"

[@encapsulatio]

Which LLM from all you tested up to now(in general, not only the ones you talked about in this video) is the best at this moment at breaking down subjects that are at a university level using pedagogical tools? If I request the model to read 2-3 books on pedagogical tools can it properly learn how to use these tools and actually apply them on explaining clearer and better the subjects?

[@lucidateAI]

This video is focused on which models perform the best at generating source code (that is to say Java, C++, python etc.). On the other hand the subject of this video -> Text Summarisation Showdown: Evaluating the Top Large Language Models (LLMs)
ua-cam.com/video/8r9h4KBLNao/v-deo.html is on text generation/translation/summarization etc. Perhaps the other video is more what you are looking for? In either event the key takeaway is that by all means rely on public, published benchmarks. But if you want to evaluate models on your specific use-case (and if I correctly understand your question, I think you do) then it might be worth considering setting up your own tests and your own benchmarks for your own specific evaluation. Clearly there is a trade off here. Setting up custom benchmarks and tests isn’t free. But if you understand how to build AI models, then it isn’t that complex either.

[@encapsulatio]

@@lucidateAI I reformulated a bit my inquiry since it was not clear enough. Can you read it again please?

[@lucidateAI]

Thanks for the clarification. The challenge with reading 2 or 3 books will be the size of the LLMs context window (the amount of tokens that can be input at once). Solutions to this involve using vector databases - example here -> ua-cam.com/video/jP9swextW2o/v-deo.html This involves writing Python code and development frameworks like LangChain. You may be an expert at this, in which case I'd recommend some of the latest Llama models and GPT-4. Alternatively you can use Gemini and Claude 3 and feed in sections of the books at a time (up to the token limit of the LLM). These models tend to perform the best when it comes to breaking down complex, university-level subjects. They seem to have a strong grasp of pedagogical principles and can structure explanations in a clear, easy-to-follow manner.
That said, I haven't specifically tested having the models read books on pedagogical tools and then applying those techniques. It's an interesting idea though! Given the understanding these advanced models already seem to have, I suspect that focused training on pedagogical methods could further enhance their explanatory abilities.
My recommendation would be to experiment with a few different models, providing them with sample content from the books and seeing how well they internalize and apply the techniques. You could evaluate the outputs to determine which model best suits your needs.