1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…