1 citations · 1 across the 6 of their papers we have counts for
6 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.…
Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA
Sergey Pletenev, Maria Marina, Nikolay Ivanov +6
Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions -- whether they…
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…
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
Maria Marina, Nikolay Ivanov, Sergey Pletenev +6
Large Language Models~(LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformatio…
How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?
Sergey Pletenev, Maria Marina, Daniil Moskovskiy +4
The performance of Large Language Models (LLMs) on many tasks is greatly limited by the knowledge learned during pre-training and stored in the model's parameters. Low-rank adaptat…
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…