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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
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Undergraduate Teaching Assistant
Introduction to Data Science, University of Maryland, Department of Computer Science, 2020
- CMSC320 in the Department of Computer Science.
Undergraduate Teaching Assistant
Introduction to Object Oriented Programming I & II, University of Maryland, Department of Computer Science, 2021
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