I recently created a demo for some prospective clients of mine, demonstrating how to use Large Language Models (LLMs) together with graph databases like Neo4J.
The two have a lot of interesting interactions, namely that you can now create knowledge graphs easier than ever before, by having AI find the graph entities and relationships from your unstructured data, rather than having to do all that manually.
On top of that, graph databases also have some advantages for Retrieval Augmented Generation (RAG) applications compared to vector search, which is currently the prevailing approach to RAG.
One of the key enablers of the ChatGPT magic can be traced back to 2017 under the obscure name of reinforcement learning with human feedback(RLHF).
Large language models(LLMs) have become one of the most interesting environments for applying modern reinforcement learning(RL) techniques. While LLMs are great at deriving knowledge from vast amounts of text, RL can help to translate that knowledge into actions. That has been the secret behind RLHF.
In this article, we will explore how we can use Llama2 for Topic Modeling without the need to pass every single document to the model. Instead, we will leverage BERTopic, a modular topic modeling technique that can use any LLM for fine-tuning topic representations.
Large language models (LLMs) have proven to be valuable tools, but they often lack reliability. Many instances have surfaced where LLM-generated responses included false information. Specifically…
Learn Prompting is the largest and most comprehensive course in prompt engineering available on the internet, with over 60 content modules, translated into 9 languages, and a thriving community.
Eversince Nov 2022, as Microsoft and OpenAI accounted ChatGTP the LLM space has been revolutionized and democratized. The demand to adopt the technology and apply it to the diverse use cases across…
OpenChat is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations. With only ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations, OpenChat is designed to achieve high performance with limited data.
We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. The code and weights, along with an online demo, are publicly available for non-commercial use.
Recent explosion in the popularity of large language models like ChatGPT has led to their increased usage in classical NLP tasks like language classification. This involves providing a context…
Perplexity is a useful metric to evaluate models in Natural Language Processing (NLP). This article will cover the two ways in which it is normally defined and the intuitions behind them. A language…
We’ve done a lot of looking over our shoulders at OpenAI. Who will cross the next milestone? What will the next move be?
But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.
I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today.
Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational - GitHub - gventuri/pandas-ai: Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational
Build document-based question-answering systems using LangChain, Pinecone, LLMs like GPT-4, and semantic search for precise, context-aware AI solutions.