Zobrazeno 1 - 10
of 2 941
pro vyhledávání: '"Chirkova, A."'
We focus on multi-domain Neural Machine Translation, with the goal of developing efficient models which can handle data from various domains seen during training and are robust to domains unseen during training. We hypothesize that Sparse Mixture-of-
Externí odkaz:
http://arxiv.org/abs/2407.01126
Autor:
Rau, David, Déjean, Hervé, Chirkova, Nadezhda, Formal, Thibault, Wang, Shuai, Nikoulina, Vassilina, Clinchant, Stéphane
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve an intricate number of different configurat
Externí odkaz:
http://arxiv.org/abs/2407.01102
Autor:
Chirkova, Nadezhda, Rau, David, Déjean, Hervé, Formal, Thibault, Clinchant, Stéphane, Nikoulina, Vassilina
Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and improving LLM factuality, but is predominantly studied in English-only s
Externí odkaz:
http://arxiv.org/abs/2407.01463
Autor:
Amirov, Abdulkarim A., Permyakova, Elizaveta S., Yusupov, Dibir M., Savintseva, Irina V., Murliev, Eldar K., Rabadanov, Kamil Sh., Popov, Anton L., Chirkova, Alisa M., Aliev, Akhmed M.
The ability to control by physical properties of the thermoresponsive polymer of PNIPAM by magnetocaloric effect was demonstrated by in-situ experiments on PNIPAM/FeRh smart composite. The concept of drug release loaded in smart composite by applying
Externí odkaz:
http://arxiv.org/abs/2406.08696
Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer i
Externí odkaz:
http://arxiv.org/abs/2402.14778
Zero-shot cross-lingual knowledge transfer enables a multilingual pretrained language model, finetuned on a task in one language, make predictions for this task in other languages. While being broadly studied for natural language understanding tasks,
Externí odkaz:
http://arxiv.org/abs/2402.12279
Zero-shot cross-lingual knowledge transfer enables the multilingual pretrained language model (mPLM), finetuned on a task in one language, make predictions for this task in other languages. While being broadly studied for natural language understandi
Externí odkaz:
http://arxiv.org/abs/2310.09917
Autor:
Chirkova, Nadezhda, Troshin, Sergey
Recent works have widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of various Transformer-based language model architectures for source code. Thi
Externí odkaz:
http://arxiv.org/abs/2308.00683
Autoregressive language models (LMs) map token sequences to probabilities. The usual practice for computing the probability of any character string (e.g. English sentences) is to first transform it into a sequence of tokens that is scored by the mode
Externí odkaz:
http://arxiv.org/abs/2306.17757
Autor:
Adabifiroozjaei, Esmaeil, Maccari, Fernando, Schaefer, Lukas, Jiang, Tianshu, Recalde-Benitez, Oscar, Chirkova, Alisa, Shayanfar, Navid, Dirba, Imants, Kani, Nagaarjhuna A, Shuleshova, Olga, Winkler, Robert, Zintler, Alexander, Rao, Ziyuan, Pfeuffer, Lukas, Kovacs, Andras, Dunin-Borkowski, Rafal E, Farle, Michael, Skokov, Konstantin, Gault, Baptiste, Gruner, Markus, Gutfleisch, Oliver, Molina-Luna, Leopoldo
Metallic/intermetalic materials with BCC structures hold an intrinsic instability due to phonon softening along [110] dirrection, causing BCC to lower-symmetry phases transformation when the BCC structures are thermally or mechanically stressed. Fe50
Externí odkaz:
http://arxiv.org/abs/2305.01351