закладки (спрятать)56
- Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources
- As data scientists, we spend a lot of our time doing exploratory data analysis (EDA), cleaning data and making sure the data we use to generate insights is of good quality. Have you ever found…
- This talk explores the integration of Knowledge Graphs (KGs) and Large Language Models (LLM) to harness their combined power for improved natural language understanding. By leveraging KGs' structured knowledge and language models' text comprehension abilities, we can leverage the domain-specific–and potentially sensitive–data together with the general knowledge of LLMs. We also examine how language models can enhance KGs through knowledge extraction and refinement. The integration of these technologies presents opportunities in various domains, from question-answering to chatbots, fostering more intelligent and context-aware applications.
- 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…
- Language Model Query Language
- 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.
- A reference architecture for the LLM app stack. It shows the most common systems, tools, and design patterns used by AI startups and tech companies.
- In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction. In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
- At the very beginning of the tutorial, I’ll explain the dimensionality of a dataset, what dimensionality reduction means, the main approaches to dimensionality reduction, the reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic of PCA by implementing the PCA algorithm with the Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast.
- The pulearn Python package provide a collection of scikit-learn wrappers to several positive-unlabled learning (PU-learning) methods. Features Scikit-learn compliant wrappers to prominent PU-learning methods. Fully tested on Linux, macOS and Windows systems. Compatible with Python 3.5+.
- 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.
- In the ever-evolving world of technology, natural language processing (NLP) and artificial intelligence (AI) have been turning heads with their jaw-dropping advancements. One of the standout players…
- Topic modelling refers to the task of identifying topics that best describes a set of documents. These topics will only emerge during the topic modelling process (therefore called latent). And one…
- 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…
публикации (спрятать)145
- (ноября 2023)
- (2024)
- CoRR, (2019)
- (2018)
- CoRR, (2019)
- (2023)
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, Association for Computing Machinery, (2023)
- (2023)
- (2023)
- CoRR, (2020)
- ECML/PKDD (2), том 11907 из Lecture Notes in Computer Science, стр. 681-696. Springer, (2019)
- PLOS ONE, 17 (9): 1-34 (сентября 2022)
- CoRR, (2021)
- (июня 2023)
- CoRR, (2020)
- CoRR, (2019)
- (2021)cite arxiv:2106.09685Comment: Draft V2 includes better baselines, experiments on GLUE, and more on adapter latency.
- (2023)cite arxiv:2303.12712.
- ACM Computing Surveys (CSUR), 54 (6): 1--35 (2021)
- том 1112 из Communications in Computer and Information Science, Springer, (2019)
