Zobrazeno 1 - 10
of 8 787
pro vyhledávání: '"Karpińska A"'
Autor:
Śliwerski, Bogusław
Publikováno v:
Studia z Teorii Wychowania / Studies on the Theory of Education. XIII(4 (41)):447-456
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1109723
Autor:
Cubrzyńska-Leonarczyk, Maria
Publikováno v:
Z Badań nad Książką i Księgozbiorami Historycznymi / Studies into the History of the Book and Book Collections. 15(4):571-579
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1008673
Autor:
Kocmi, Tom, Avramidis, Eleftherios, Bawden, Rachel, Bojar, Ondrej, Dvorkovich, Anton, Federmann, Christian, Fishel, Mark, Freitag, Markus, Gowda, Thamme, Grundkiewicz, Roman, Haddow, Barry, Karpinska, Marzena, Koehn, Philipp, Marie, Benjamin, Murray, Kenton, Nagata, Masaaki, Popel, Martin, Popovic, Maja, Shmatova, Mariya, Steingrímsson, Steinþór, Zouhar, Vilém
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking and supersedes it. The purpose of this report is not to interpret any
Externí odkaz:
http://arxiv.org/abs/2407.19884
Autor:
Burešová, Jana
Publikováno v:
Historica - Sborník prací historických / Historica. Acta Universitatis Palackianae Olomucensis - Facultas philosophica. XLVII(57):267-269
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=823196
Autor:
Arora, Shane, Karpinska, Marzena, Chen, Hung-Ting, Bhattacharjee, Ipsita, Iyyer, Mohit, Choi, Eunsol
Large language models (LLMs) are used for long-form question answering (LFQA), which requires them to generate paragraph-length answers to complex questions. While LFQA has been well-studied in English, this research has not been extended to other la
Externí odkaz:
http://arxiv.org/abs/2406.17761
Synthetic long-context LLM benchmarks (e.g., "needle-in-the-haystack") test only surface-level retrieval capabilities, but how well can long-context LLMs retrieve, synthesize, and reason over information across book-length inputs? We address this que
Externí odkaz:
http://arxiv.org/abs/2406.16264
Autor:
Kocmi, Tom, Zouhar, Vilém, Avramidis, Eleftherios, Grundkiewicz, Roman, Karpinska, Marzena, Popović, Maja, Sachan, Mrinmaya, Shmatova, Mariya
High-quality Machine Translation (MT) evaluation relies heavily on human judgments. Comprehensive error classification methods, such as Multidimensional Quality Metrics (MQM), are expensive as they are time-consuming and can only be done by experts,
Externí odkaz:
http://arxiv.org/abs/2406.11580
Autor:
Kim, Yekyung, Chang, Yapei, Karpinska, Marzena, Garimella, Aparna, Manjunatha, Varun, Lo, Kyle, Goyal, Tanya, Iyyer, Mohit
Publikováno v:
1st Conference on Language Modeling (COLM 2024)
While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited evaluations of input-dependent aspects like faithfulness. In this paper,
Externí odkaz:
http://arxiv.org/abs/2404.01261
Autor:
Nakamura, Taishi, Mishra, Mayank, Tedeschi, Simone, Chai, Yekun, Stillerman, Jason T, Friedrich, Felix, Yadav, Prateek, Laud, Tanmay, Chien, Vu Minh, Zhuo, Terry Yue, Misra, Diganta, Bogin, Ben, Vu, Xuan-Son, Karpinska, Marzena, Dantuluri, Arnav Varma, Kusa, Wojciech, Furlanello, Tommaso, Yokota, Rio, Muennighoff, Niklas, Pai, Suhas, Adewumi, Tosin, Laippala, Veronika, Yao, Xiaozhe, Junior, Adalberto, Ariyak, Alpay, Drozd, Aleksandr, Clive, Jordan, Gupta, Kshitij, Chen, Liangyu, Sun, Qi, Tsui, Ken, Persaud, Noah, Fahmy, Nour, Chen, Tianlong, Bansal, Mohit, Monti, Nicolo, Dang, Tai, Luo, Ziyang, Bui, Tien-Tung, Navigli, Roberto, Mehta, Virendra, Blumberg, Matthew, May, Victor, Nguyen, Huu, Pyysalo, Sampo
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community devel
Externí odkaz:
http://arxiv.org/abs/2404.00399
Autor:
Sivak, Elizaveta, Pankowska, Paulina, Mendrik, Adrienne, Emery, Tom, Garcia-Bernardo, Javier, Hocuk, Seyit, Karpinska, Kasia, Maineri, Angelica, Mulder, Joris, Nissim, Malvina, Stulp, Gert
The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predic
Externí odkaz:
http://arxiv.org/abs/2402.00705