Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Artemova Ekaterina"'
Autor:
Chernyshev, Konstantin, Polshkov, Vitaliy, Artemova, Ekaterina, Myasnikov, Alex, Stepanov, Vlad, Miasnikov, Alexei, Tilga, Sergei
The current evaluation of mathematical skills in LLMs is limited, as existing benchmarks are either relatively small, primarily focus on elementary and high-school problems, or lack diversity in topics. Additionally, the inclusion of visual elements
Externí odkaz:
http://arxiv.org/abs/2412.03205
Autor:
Artemova, Ekaterina, Tsvigun, Akim, Schlechtweg, Dominik, Fedorova, Natalia, Tilga, Sergei, Obmoroshev, Boris
Training and deploying machine learning models relies on a large amount of human-annotated data. As human labeling becomes increasingly expensive and time-consuming, recent research has developed multiple strategies to speed up annotation and reduce
Externí odkaz:
http://arxiv.org/abs/2411.04637
Autor:
Artemova, Ekaterina, Lucas, Jason, Venkatraman, Saranya, Lee, Jooyoung, Tilga, Sergei, Uchendu, Adaku, Mikhailov, Vladislav
The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and ma
Externí odkaz:
http://arxiv.org/abs/2411.04032
Autor:
Abassy, Mervat, Elozeiri, Kareem, Aziz, Alexander, Ta, Minh Ngoc, Tomar, Raj Vardhan, Adhikari, Bimarsha, Ahmed, Saad El Dine, Wang, Yuxia, Afzal, Osama Mohammed, Xie, Zhuohan, Mansurov, Jonibek, Artemova, Ekaterina, Mikhailov, Vladislav, Xing, Rui, Geng, Jiahui, Iqbal, Hasan, Mujahid, Zain Muhammad, Mahmoud, Tarek, Tsvigun, Akim, Aji, Alham Fikri, Shelmanov, Artem, Habash, Nizar, Gurevych, Iryna, Nakov, Preslav
The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, part
Externí odkaz:
http://arxiv.org/abs/2408.04284
This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze t
Externí odkaz:
http://arxiv.org/abs/2407.17629
Autor:
Taktasheva, Ekaterina, Bazhukov, Maxim, Koncha, Kirill, Fenogenova, Alena, Artemova, Ekaterina, Mikhailov, Vladislav
Minimal pairs are a well-established approach to evaluating the grammatical knowledge of language models. However, existing resources for minimal pairs address a limited number of languages and lack diversity of language-specific grammatical phenomen
Externí odkaz:
http://arxiv.org/abs/2406.19232
This paper describes AIpom, a system designed to detect a boundary between human-written and machine-generated text (SemEval-2024 Task 8, Subtask C: Human-Machine Mixed Text Detection). We propose a two-stage pipeline combining predictions from an in
Externí odkaz:
http://arxiv.org/abs/2403.19354
Warning: this work contains upsetting or disturbing content. Large language models (LLMs) tend to learn the social and cultural biases present in the raw pre-training data. To test if an LLM's behavior is fair, functional datasets are employed, and d
Externí odkaz:
http://arxiv.org/abs/2403.17553
Autor:
Peng, Siyao, Sun, Zihang, Shan, Huangyan, Kolm, Marie, Blaschke, Verena, Artemova, Ekaterina, Plank, Barbara
Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. This paper introduces the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavar
Externí odkaz:
http://arxiv.org/abs/2403.12749
Mainstream cross-lingual task-oriented dialogue (ToD) systems leverage the transfer learning paradigm by training a joint model for intent recognition and slot-filling in English and applying it, zero-shot, to other languages. We address a gap in pri
Externí odkaz:
http://arxiv.org/abs/2402.02078