Zobrazeno 1 - 10
of 873
pro vyhledávání: '"P. Bielikova"'
Prompt tuning is a modular and efficient solution for training large language models (LLMs). One of its main advantages is task modularity, making it suitable for multi-task problems. However, current soft-prompt-based methods often sacrifice multi-t
Externí odkaz:
http://arxiv.org/abs/2408.01119
While fine-tuning of pre-trained language models generally helps to overcome the lack of labelled training samples, it also displays model performance instability. This instability mainly originates from randomness in initialisation or data shuffling
Externí odkaz:
http://arxiv.org/abs/2406.12471
While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-called randomness factors (e.g., varying order of data). We propose a metho
Externí odkaz:
http://arxiv.org/abs/2402.12817
When solving NLP tasks with limited labelled data, researchers can either use a general large language model without further update, or use a small number of labelled examples to tune a specialised smaller model. In this work, we address the research
Externí odkaz:
http://arxiv.org/abs/2402.12819
In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the limited number of samples used to train a model have a significant impact on the overall success. Although a large number of sample selection strategies exi
Externí odkaz:
http://arxiv.org/abs/2402.03038
Autor:
Macko, Dominik, Moro, Robert, Uchendu, Adaku, Srba, Ivan, Lucas, Jason Samuel, Yamashita, Michiharu, Tripto, Nafis Irtiza, Lee, Dongwon, Simko, Jakub, Bielikova, Maria
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-generated text (MGT) detection is important to cope with such threats. H
Externí odkaz:
http://arxiv.org/abs/2401.07867
Autor:
Cegin, Jan, Pecher, Branislav, Simko, Jakub, Srba, Ivan, Bielikova, Maria, Brusilovsky, Peter
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to asse
Externí odkaz:
http://arxiv.org/abs/2401.06643
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a small amount of labelled samples. However, these approaches have been observe
Externí odkaz:
http://arxiv.org/abs/2312.01082
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies a
Externí odkaz:
http://arxiv.org/abs/2311.08838
Autor:
Macko, Dominik, Moro, Robert, Uchendu, Adaku, Lucas, Jason Samuel, Yamashita, Michiharu, Pikuliak, Matúš, Srba, Ivan, Le, Thai, Lee, Dongwon, Simko, Jakub, Bielikova, Maria
Publikováno v:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings. This is also reflected in the available ben
Externí odkaz:
http://arxiv.org/abs/2310.13606