Alexander Panchenko
6 papers in the PaperMetrix corpus
Papers by this author
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RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
2018 · MADOC (University of Mannheim)
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Romance and Germanic languages, we …
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Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language
2022 · arXiv (Cornell University)
Toxicity on the Internet, such as hate speech, offenses towards particular users or groups of people, or the use of obscene words, is an acknowledged problem. However, there also exist other types of inappropriate messages …
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LM-Polygraph: Uncertainty Estimation for Language Models
2023 · arXiv (Cornell University)
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often "hallucinate", i.e., fabricate …
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Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
2024 · arXiv (Cornell University)
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of …
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Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
2024 · arXiv (Cornell University)
The rapid proliferation of large language models (LLMs) has stimulated researchers to seek effective and efficient approaches to deal with LLM hallucinations and low-quality outputs. Uncertainty quantification (UQ) is a key element of machine learning …
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Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
2025 · arXiv (Cornell University)
Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. Recent …