activity
20202026
most citedMental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

28 citations · 82 across the 33 of their papers we have counts for

collaborators

36 papers

cs.CL2026

Why Summaries Turn Neutral: Policy Attribution for Sentiment Drift in Reinforcement Learning from Human Feedback

Mikhail Krasitskii, Alexander Gelbukh, Olga Kolesnikova +1

Reinforcement learning with human feedback (RLHF) aligns LLMs with human preferences, improving summarization fluency and safety, but causes sentiment drift: overly neutral summari…

cs.CL2026

Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance

Mikhail Krasitskii, Alexander Gelbukh, Olga Kolesnikova +1

Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affectiv…

cs.CL2025

Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models

Fiaz Ahmad, Nisar Hussain, Amna Qasim +3

Ironic identification is a challenging task in Natural Language Processing, particularly when dealing with languages that differ in syntax and cultural context. In this work, we ai…

cs.CL2025

Bilingual Word Level Language Identification for Omotic Languages

Mesay Gemeda Yigezu, Girma Yohannis Bade, Atnafu Lambebo Tonja +3

Language identification is the task of determining the languages for a given text. In many real world scenarios, text may contain more than one language, particularly in multilingu…

cs.IR2025

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning

Anvi Alex Eponon, Moein Shahiki-Tash, Ildar Batyrshin +3

This study presents a question-based knowledge encoding approach that improves retrieval-augmented generation (RAG) systems without requiring fine-tuning or traditional chunking. W…

cs.CL2025

Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis

Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova +2

We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model…