Zobrazeno 1 - 10
of 5 918
pro vyhledávání: '"A. A. Artemova"'
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
Autor:
Saidov, Marat, Bakalova, Aleksandra, Taktasheva, Ekaterina, Mikhailov, Vladislav, Artemova, Ekaterina
The evaluation of Natural Language Generation (NLG) models has gained increased attention, urging the development of metrics that evaluate various aspects of generated text. LUNA addresses this challenge by introducing a unified interface for 20 NLG
Externí odkaz:
http://arxiv.org/abs/2401.04522
Instruction tuning has become an integral part of training pipelines for Large Language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of research, Annotation Error Detection (AED) has emerged as a tool for
Externí odkaz:
http://arxiv.org/abs/2309.01669
Publikováno v:
ТРАДИЦИОННАЯ КУЛЬТУРА. :184-187
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12a4d6b82640cd0ef007665b6dde6c78
https://doi.org/10.26158/tk.2021.22.4.018
https://doi.org/10.26158/tk.2021.22.4.018
Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminis
Externí odkaz:
http://arxiv.org/abs/2305.05295