Papers by Konstantin Polev
Hallucination Detection in LLMs with Topological Divergence on Attention Graphs (2026.acl-long)
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Alexandra Bazarova, Andrei Volodichev, Aleksandr Yugay, Andrey Shulga, Alina Ermilova, Konstantin Polev, Julia Belikova, Rauf Parchiev, Dmitry Simakov, Maxim Savchenko, Andrey Savchenko, Serguei Barannikov, Alexey Zaytsev
| Challenge: | Large language models (LLMs) are prone to producing so-called hallucinations, i.e., content that is factually or contextually incorrect. |
| Approach: | They propose a TOpology-based HAllucination detector which quantifies the structural properties of graphs induced by attention matrices. |
| Outcome: | The proposed detector achieves state-of-the-art or competitive results on several benchmarks while requiring minimal annotated data and computational resources. |
Detecting Overflow in Compressed Token Representations for Retrieval-Augmented Generation (2026.eacl-srw)
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| Challenge: | Efficient long-context processing remains a challenge for large language models (LLMs) however, the limits of compressibility remain underexplored. |
| Approach: | They propose a method to characterize and detect token overflow in xRAG soft-compression by mapping long contexts into dense vectors that can be directly consumed by the model. |
| Outcome: | The proposed method identifies token overflow with query-agnostic saturation statistics but lacks the capability to detect it. |