DeepPavlov

Hello everyone,

Recently we published an article “Memory enhances ChatGPT performance in multi-hop QA”. The article discusses the approach for ChatGPT memory augmentation and compares it to the task-tuned language model on multi-hop QA tasks.

Check out the full article, and don’t hesitate to contact us if you have any questions or comments!

👉 https://medium.com/deeppavlov/memory-enhances-chatgpt-performance-in-multi-hop-qa-1ec4a5f89cb1?source=friends_link&sk=f9be266b694aa169e5195c673c6b25a9

Memory enhances ChatGPT performance in multi-hop QA An assessment of ChatGPT performance on MHQA tasks with and without memory augmentation compared to a task-tuned language model.

Hello everyone!

We are excited to announce our new article about multi-task models in DeepPavlov. These models allow combining many tasks into one backbone while retaining or even increasing the model quality.

Check out the full article, and don’t hesitate to contact us if you have any questions or comments!

👉 https://clck.ru/35akGg

Hello everyone!

We are excited to announce the of DeepPavlov 1.3.0 🎅. Here is a sneak peak of what's included in the new release:
◾️ A few-shot text classification model that allows you to operate with a small amount of training data and supports OOS examples.
◾️ A refactored ODQA model optimized to consume less RAM during training.
◾️ A long-anticipated syntax parser and morphotagger.

As a reminder, you can check our multilingual models at the DeepPavlov Demo page. Be the first to try out the DeepPavlov 1.3.0 release and don’t hesitate to share your feedback with us!

https://github.com/deeppavlov/DeepPavlov/releases/tag/1.3.0

Hello everyone,

We are back with some fantastic news: a brand new demo of DeepPavlov Library just came out, featuring a lot of new, much needed multilingual models. Be the first to try it out and don’t hesitate to share your feedback with us!

As you probably know, the NLP field is highly dynamic, and with the current pace of change we’d like to hear from you to learn how else we could help you solve your everyday natural language processing tasks; if you’re up for it, we’d highly appreciate your filling out a short survey!

Demo DeepPavlov: https://demo.deeppavlov.ai/ #/examples/text_intent

Short survey: https://forms.gle/RkG5ie9xYHBfdDxS9

Help us to make DeepPavlov Library better! This form is anonymous. However, if you would like to stay in touch with the DeepPavlov Library team please provide us with your contact details (in the last question). Thank you!

Calling interested in ! We conduct System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.
TIME FOR TUTORIALS ON DREAM!
Create a distribution for Question Answering based on large documents using TF-IDF vectrorization to select most relevant parts and ChatGPT to generate responses based on them.

Creating Assistants with DeepPavlov Dream. Part 4: Document-based Question Answering with LLMs Learn how to use or customize Document-based Question Answering distribution of Dream chatbot.

Calling interested in ! We conduct System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.
TIME FOR TUTORIALS ON DREAM!
Create a distribution with reasoning and API calls performing utilizing OpenAI ChatGPT to think of actions required to handle user requests and to choose the suitable API to use.

Creating Assistants with DeepPavlov Dream. Welcome to our third tutorial, where we will walk you through the process of building your own personal assistant using DeepPavlov Dream…

Calling interested in ! We conduct System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.
TIME FOR TUTORIALS ON DREAM!
Discover how to create a generative bot with a predefined persona, using the existing Dream Persona distribution that utilizes OpenAI ChatGPT.

Creating Assistants with DeepPavlov Dream. Part 2: Customizing Prompt-based Distribution Introduction

Calling interested in ! We conduct System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.
TIME FOR TUTORIALS ON DREAM!
Learn how to create a simple and light-weight bot that specializes in discussing movies and answering factoid questions, utilizing the existing Dream components.

Creating Assistants with DeepPavlov Dream. Part 1: Distribution from Existing Components Introduction

Calling all attendees! Don't miss our System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.
If you are not at ACL, join our DeepPavlov Discord
https://lnkd.in/eGgufSPm

10 July @ 14:00
Demo Session 2 @ Frontenac Ballroom, Queens Quay&Bay

Join the DeepPavlov Discord Server! Check out the DeepPavlov community on Discord - hang out with 24 other members and enjoy free voice and text chat.

Calling all attendees!
Don't miss our System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants.

10 July @ 14:00
Demo Session 2 @ Frontenac Ballroom, Queens Quay&Bay

Discover how our platform prioritizes efficiency, modularity, scalability, and extensibility, making it easier to develop complex dialog systems from scratch. Explore our rich library of NLP components and learn how to create scalable, industrial-grade AI assistants.

Dream web-site https://dream.deeppavlov.ai
DeepPavlov Discord https://discord.gg/BY4hB9V2J
List of available Guides https://docs.dream.deeppavlov.ai/en/guides

DeepPavlov Dream Create multi-skill Generative AI Assistants with DeepPavlov Dream

📣 Calling all attendees! 🚀 Don't miss our System Demo on the open-source DeepPavlov Dream, designed for building complex, generative AI Assistants. 🤖💬
10 July @ 14:00
Demo Session 2 @ Frontenac Ballroom, Queens Quay&Bay
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Ever wondered how stacks up against traditional models? 🤔 The results might surprise you! 🚀 Dive into this insightful analysis to discover the strengths and weaknesses of ChatGPT on Q&A tasks. Will it outshine the rest? 🌟



Find out all the details 👉🏻

How good is ChatGPT on QA tasks? A hands-on comparison using ChatGPT and fine-tuned encoder-based models on QA tasks.