Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

About me

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Large Language Models Meet Knowledge Graphs to Answer Factoid Questions

Published in Pacific Asia Conference on Language, Information and Computation (PACLIC 37), 2023

Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper, we propose a method for exploring pre-trained Text-to-Text Language Models enriched with additional information from Knowledge Graphs for answering factoid questions. More specifically, we propose an algorithm for subgraphs extraction from a Knowledge Graph based on question entities and answer candidates. Then, we procure easily interpreted information with Transformer-based models through the linearization of the extracted subgraphs. Final re-ranking of the answer candidates with the extracted information boosts Hits@1 scores of the pre-trained text-to-text language models by 4-6%.

Recommended citation: Salnikov et. al (2023). "Large Language Models Meet Knowledge Graphs to Answer Factoid Questions" In proceedings of Pacific Asia Conference on Language, Information and Computation (PACLIC 2023). https://arxiv.org/abs/2310.02166

talks

teaching

Computer Science Instructor

Introduction to Python & Programming, Geeklama, 2022

  • Taught students Introduction to Python Programming and rudimentary computer science concepts/ideas such as expressions, statements, methods, conditionals, and others.

Invited Lecturer

Evolution of Large Language Models, Nationals Research University, Higher School of Economics, 2022

  • Held a mini-course on rudimentary Natural Language Processing concepts and the current state-of-the-arts in the scope of machine translation. Different topics include: word embeddings, transformer and attention architecture, encoder-only, decoder-only and encoder-decoder architectures