5 papers
When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Elisei Rykov, Kseniia Petrushina, Maksim Savkin +6
Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy.…
Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images
Elisei Rykov, Kseniia Petrushina, Kseniia Titova +3
Measuring how real images look is a complex task in artificial intelligence research. For example, an image of a boy with a vacuum cleaner in a desert violates common sense. We int…
Don't Fight Hallucinations, Use Them: Estimating Image Realism using NLI over Atomic Facts
Elisei Rykov, Kseniia Petrushina, Kseniia Titova +2
Quantifying the realism of images remains a challenging problem in the field of artificial intelligence. For example, an image of Albert Einstein holding a smartphone violates comm…
S3: A Simple Strong Sample-effective Multimodal Dialog System
Elisei Rykov, Egor Malkershin, Alexander Panchenko
In this work, we present a conceptually simple yet powerful baseline for the multimodal dialog task, an S3 model, that achieves near state-of-the-art results on two compelling lead…
SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection
Elisei Rykov, Yana Shishkina, Kseniia Petrushina +3
In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions…