Zobrazeno 1 - 10
of 251
pro vyhledávání: '"MULLER, BENJAMIN"'
Autor:
Bandarkar, Lucas, Muller, Benjamin, Yuvraj, Pritish, Hou, Rui, Singhal, Nayan, Lv, Hongjiang, Liu, Bing
Model merging, such as model souping, is the practice of combining different models with the same architecture together without further training. In this work, we present a model merging methodology that addresses the difficulty of fine-tuning Large
Externí odkaz:
http://arxiv.org/abs/2410.01335
Autor:
Nguyen, Tu Anh, Muller, Benjamin, Yu, Bokai, Costa-jussa, Marta R., Elbayad, Maha, Popuri, Sravya, Ropers, Christophe, Duquenne, Paul-Ambroise, Algayres, Robin, Mavlyutov, Ruslan, Gat, Itai, Williamson, Mary, Synnaeve, Gabriel, Pino, Juan, Sagot, Benoit, Dupoux, Emmanuel
We introduce Spirit LM, a foundation multimodal language model that freely mixes text and speech. Our model is based on a 7B pretrained text language model that we extend to the speech modality by continuously training it on text and speech units. Sp
Externí odkaz:
http://arxiv.org/abs/2402.05755
Autor:
Yu, Lili, Shi, Bowen, Pasunuru, Ramakanth, Muller, Benjamin, Golovneva, Olga, Wang, Tianlu, Babu, Arun, Tang, Binh, Karrer, Brian, Sheynin, Shelly, Ross, Candace, Polyak, Adam, Howes, Russell, Sharma, Vasu, Xu, Puxin, Tamoyan, Hovhannes, Ashual, Oron, Singer, Uriel, Li, Shang-Wen, Zhang, Susan, James, Richard, Ghosh, Gargi, Taigman, Yaniv, Fazel-Zarandi, Maryam, Celikyilmaz, Asli, Zettlemoyer, Luke, Aghajanyan, Armen
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows th
Externí odkaz:
http://arxiv.org/abs/2309.02591
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
Autor:
Muller, Benjamin, Alastruey, Belen, Hansanti, Prangthip, Kalbassi, Elahe, Ropers, Christophe, Smith, Eric Michael, Williams, Adina, Zettlemoyer, Luke, Andrews, Pierre, Costa-jussà, Marta R.
Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation data. Despite recent progress in documenting this problem and many atte
Externí odkaz:
http://arxiv.org/abs/2308.16871
Autor:
Muller, Benjamin, Wieting, John, Clark, Jonathan H., Kwiatkowski, Tom, Ruder, Sebastian, Soares, Livio Baldini, Aharoni, Roee, Herzig, Jonathan, Wang, Xinyi
Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingu
Externí odkaz:
http://arxiv.org/abs/2305.14332
Multilingual generative language models (LMs) are increasingly fluent in a large variety of languages. Trained on the concatenation of corpora in multiple languages, they enable powerful transfer from high-resource languages to low-resource ones. How
Externí odkaz:
http://arxiv.org/abs/2302.12299
Autor:
Muller, Benjamin, Gupta, Deepanshu, Patwardhan, Siddharth, Fauconnier, Jean-Philippe, Vandyke, David, Agarwal, Sachin
Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better
Externí odkaz:
http://arxiv.org/abs/2212.01757
Open-Domain Generative Question Answering has achieved impressive performance in English by combining document-level retrieval with answer generation. These approaches, which we refer to as GenQA, can generate complete sentences, effectively answerin
Externí odkaz:
http://arxiv.org/abs/2110.07150
Autor:
Diamant, Samuel, Cafarelli, Laurine, Goetsch, Thibaut, Muller, Benjamin, Liverneaux, Philippe
Publikováno v:
In Revue de Chirurgie Orthopedique et Traumatologique June 2024 110(4):575-582
