5 papers
Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark
Viktor Moskvoretskii, Alina Lobanova, Ekaterina Neminova +3
This paper explores the feasibility of using text-to-image models in a zero-shot setup to generate images for taxonomy concepts. While text-based methods for taxonomy enrichment ar…
Self-Taught Self-Correction for Small Language Models
Viktor Moskvoretskii, Chris Biemann, Irina Nikishina
Although large language models (LLMs) have achieved remarkable performance across various tasks, they remain prone to errors. A key challenge is enabling them to self-correct. Whil…
Argument-Based Comparative Question Answering Evaluation Benchmark
Irina Nikishina, Saba Anwar, Nikolay Dolgov +6
In this paper, we aim to solve the problems standing in the way of automatic comparative question answering. To this end, we propose an evaluation framework to assess the quality o…
Creating a Taxonomy for Retrieval Augmented Generation Applications
Irina Nikishina, Özge Sevgili, Mahei Manhai Li +2
In this research, we develop a taxonomy to conceptualize a comprehensive overview of the constituting characteristics that define retrieval augmented generation (RAG) applications,…
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann +3
We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We…