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cs.CL2026
Progressive Cramming: Reliable Token Compression and What It Reveals
Dmitrii Tarasov, Timofei Lashukov, Elizaveta Goncharova +1
Token cramming compresses sequences into learned embeddings with near-perfect reconstruction, but fixed token budgets and 99\% accuracy thresholds leave it unclear whether residual…
cs.CL2025
Sentence-Anchored Gist Compression for Long-Context LLMs
Dmitrii Tarasov, Elizaveta Goncharova, Kuznetsov Andrey
This work investigates context compression for Large Language Models (LLMs) using learned compression tokens to reduce the memory and computational demands of processing long seque…
cs.CL2025
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
Viktoriia Chekalina, Anton Razzhigaev, Elizaveta Goncharova +1
In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves…