Papers by Alexey Zaytsev
Complexity-aware fine-tuning (2026.findings-eacl)
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| Challenge: | General-purpose Large Language Models (LLMs) are often fine-tuned through supervised fine- tuning (SFT) to enhance performance in specific domains. |
| Approach: | They propose a novel approach that uses reasoning only for complex data identified by entropy to refine large language models. |
| Outcome: | The proposed model outperforms the standard SFT approach while using 81% less data. |
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. |