Zobrazeno 1 - 10
of 1 491
pro vyhledávání: '"Artetxe, A."'
Autor:
Zampella, Maria, Otamendi, Urtzi, Belaunzaran, Xabier, Artetxe, Arkaitz, Olaizola, Igor G., Longo, Giuseppe, Sierra, Basilio
Scheduling problems pose significant challenges in resource, industry, and operational management. This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent Reinforcement Learning
Externí odkaz:
http://arxiv.org/abs/2411.07634
Autor:
Sánchez, Eduardo, Alastruey, Belen, Ropers, Christophe, Stenetorp, Pontus, Artetxe, Mikel, Costa-jussà, Marta R.
We propose a new benchmark to measure a language model's linguistic reasoning skills without relying on pre-existing language-specific knowledge. The test covers 894 questions grouped in 160 problems across 75 (mostly) extremely low-resource language
Externí odkaz:
http://arxiv.org/abs/2409.12126
Large Language Models (LLMs) exhibit extensive knowledge about the world, but most evaluations have been limited to global or anglocentric subjects. This raises the question of how well these models perform on topics relevant to other cultures, whose
Externí odkaz:
http://arxiv.org/abs/2406.07302
Autor:
Padlewski, Piotr, Bain, Max, Henderson, Matthew, Zhu, Zhongkai, Relan, Nishant, Pham, Hai, Ong, Donovan, Aleksiev, Kaloyan, Ormazabal, Aitor, Phua, Samuel, Yeo, Ethan, Lamprecht, Eugenie, Liu, Qi, Wang, Yuqi, Chen, Eric, Fu, Deyu, Li, Lei, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Artetxe, Mikel, Tay, Yi
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. Vib
Externí odkaz:
http://arxiv.org/abs/2405.02287
Autor:
Reka Team, Ormazabal, Aitor, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Fu, Deyu, Ong, Donovan, Chen, Eric, Lamprecht, Eugenie, Pham, Hai, Ong, Isaac, Aleksiev, Kaloyan, Li, Lei, Henderson, Matthew, Bain, Max, Artetxe, Mikel, Relan, Nishant, Padlewski, Piotr, Liu, Qi, Chen, Ren, Phua, Samuel, Yang, Yazheng, Tay, Yi, Wang, Yuqi, Zhu, Zhongkai, Xie, Zhihui
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of t
Externí odkaz:
http://arxiv.org/abs/2404.12387
Autor:
Etxaniz, Julen, Sainz, Oscar, Perez, Naiara, Aldabe, Itziar, Rigau, German, Agirre, Eneko, Ormazabal, Aitor, Artetxe, Mikel, Soroa, Aitor
Publikováno v:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14952--14972. 2024
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque corpus comprising 4.3M documents and 4.2B tokens. Addressing the scarci
Externí odkaz:
http://arxiv.org/abs/2403.20266
While machine translation (MT) systems have seen significant improvements, it is still common for translations to reflect societal biases, such as gender bias. Decoder-only Large Language Models (LLMs) have demonstrated potential in MT, albeit with p
Externí odkaz:
http://arxiv.org/abs/2309.03175
Autor:
Bandarkar, Lucas, Liang, Davis, Muller, Benjamin, Artetxe, Mikel, Shukla, Satya Narayan, Husa, Donald, Goyal, Naman, Krishnan, Abhinandan, Zettlemoyer, Luke, Khabsa, Madian
Publikováno v:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024
We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation o
Externí odkaz:
http://arxiv.org/abs/2308.16884
As increasingly sophisticated language models emerge, their trustworthiness becomes a pivotal issue, especially in tasks such as summarization and question-answering. Ensuring their responses are contextually grounded and faithful is challenging due
Externí odkaz:
http://arxiv.org/abs/2308.12157
Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system, and running inference over the translated input.
Externí odkaz:
http://arxiv.org/abs/2308.01223