12 papers
Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian
Aleksandr Nikolich, Konstantin Korolev, Sergei Bratchikov +2
There has been a surge in the development of various Large Language Models (LLMs). However, text generation for languages other than English often faces significant challenges, inc…
Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads
Artem Vazhentsev, Lyudmila Rvanova, Gleb Kuzmin +8
While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ)…
Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov +10
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertens…
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
Yuxia Wang, Rui Xing, Jonibek Mansurov +23
Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random…
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
Roman Vashurin, Maiya Goloburda, Albina Ilina +4
Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches, with two major types being particularly prominent: information-based, wh…
Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models
Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing +7
Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper u…