1 citations · 1 across the 8 of their papers we have counts for
8 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…
AdaMem: Adaptive Memory Token Allocation for Soft Compression in Retrieval-Augmented Generation
Artem Sakhno, Grigorii Davydenko, Omar Zoloev +3
Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce distracting information. Soft c…
SIRIN: A Unified Toolkit for Detecting Contextual Hallucinations in Retrieval-Augmented and Memory-Grounded LLM Systems
Julia Belikova, Rauf Parchiev, Mikhail Filimonov +3
SIRIN (Semantic Inconsistency Recognition and Inspection Nexus) is a unified toolkit and interactive web UI for detecting contextual hallucinations (fluent, plausible responses uns…
Managing Procedural Memory in LLM Agents: Control, Adaptation, and Evaluation
Julia Belikova, Rauf Parchiev, Evgeny Egorov +4
Procedural memory is increasingly used to improve LLM agents on recurring workplace tasks, yet its ability to produce reusable skills remains poorly understood. We introduce AFTER,…
Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation
Julia Belikova, Danila Rozhevskii, Dennis Svirin +2
Efficient long-context processing remains a crucial challenge for contemporary large language models (LLMs), especially in resource-constrained environments. Soft compression archi…
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.…
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs
Julia Belikova, Konstantin Polev, Rauf Parchiev +1
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly deployed in industry applications, yet their reliability remains hampered by challeng…
Parallel Kac's Walk Generates PRU
Chuhan Lu, Minglong Qin, Fang Song +2
Ma and Huang recently proved that the PFC construction, introduced by Metger, Poremba, Sinha and Yuen [MPSY24], gives an adaptive-secure pseudorandom unitary family PRU. Their proo…