1 citations · 1 across the 6 of their papers we have counts for
6 papers
Enoki: Efficient Multi-Level Hallucination Detection
Elisei Rykov, Timur Ionov, Nikolay Ivanov +5
Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a single level: claim-level methods…
MemToC: Benchmarking Memory-Tool Conflict Resolution in Large Language Models
Arseniy Varlamov, Rishat Zinnatullin, Elisei Rykov +2
Tool-augmented LLMs must arbitrate between two fallible sources when a tool return conflicts with their parametric memory, yet existing evaluations measure source preference withou…
Multimodal Evaluation of Russian-language Architectures
Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov +15
Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, a…
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.…
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…