Papers by Aleksandr Rubashevskii
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)
Copied to clipboard
Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts. |
| Approach: | They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme . |
| Outcome: | The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels. |
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification (2024.findings-acl)
Copied to clipboard
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov
| Challenge: | Large language models are notorious for producing erroneous claims in their output. |
| Approach: | They propose a fact-checking and hallucination detection pipeline based on token-level uncertainty quantification that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. |
| Outcome: | The proposed method can fact-check the atomic claims in the output of large language models. |
Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
Ekaterina Fadeeva, Aleksandr Rubashevskii, Dzianis Piatrashyn, Roman Vashurin, Shehzaad Dhuliawala, Artem Shelmanov, Timothy Baldwin, Preslav Nakov, Mrinmaya Sachan, Maxim Panov
| Challenge: | Existing approaches to mitigating hallucinations conflate factuality with faithfulness to the retrieved evidence, incorrectly labeling factually correct statements as hallucinos . Existing methods to mitigate hallucinics rely on a lack of training data coverage, input ambiguity, and architectural constraints. |
| Approach: | They propose a method for hallucination detection in Large Language Models enhanced with knowledge retrieval based on faithfulness to the retrieved context. |
| Outcome: | The proposed method outperforms unsupervised UQ baselines, RAG-specific methods, and supervised classifiers across multiple tasks and LLMs. |
ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts (2022.emnlp-demos)
Copied to clipboard
Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova, Dmitry V. Dylov, Igor Galitskiy, Artem Shelmanov
| Challenge: | Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. |
| Approach: | They propose an open-source framework for active learning annotation in natural language processing that provides an easy-to-deploy GUI annotation tool directly in the Jupyter IDE. |
| Outcome: | The proposed framework reduces computational overhead and duration of AL iterations and increases annotated data reusability. |