Papers by Pavel Blinov

2 papers
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)

Copied to clipboard

Challenge: Humor recognition datasets contain only English texts and focus on puns.
Approach: They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties.
Outcome: The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set.
3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark (2025.emnlp-main)

Copied to clipboard

Challenge: Large Vision-Language Models (LVLMs) are being explored in medicine but their ability to conduct complex real-world telemedicine consultations remains underexplored.
Approach: They propose to use large vision-language models to conduct telemedicine consultations using a framework that simulates patient variability and evaluates diagnostic accuracy and dialogue quality via Assessor Agent.
Outcome: The proposed framework compares diagnostic strategies for open and closed-source LVLMs and shows that multimodal dialogue improves F1 score by 6.5% over non-dialogue settings.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations