Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Campos, Jon Ander"'
Autor:
Sainz, Oscar, García-Ferrero, Iker, Jacovi, Alon, Campos, Jon Ander, Elazar, Yanai, Agirre, Eneko, Goldberg, Yoav, Chen, Wei-Lin, Chim, Jenny, Choshen, Leshem, D'Amico-Wong, Luca, Dell, Melissa, Fan, Run-Ze, Golchin, Shahriar, Li, Yucheng, Liu, Pengfei, Pahwa, Bhavish, Prabhu, Ameya, Sharma, Suryansh, Silcock, Emily, Solonko, Kateryna, Stap, David, Surdeanu, Mihai, Tseng, Yu-Min, Udandarao, Vishaal, Wang, Zengzhi, Xu, Ruijie, Yang, Jinglin
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora u
Externí odkaz:
http://arxiv.org/abs/2407.21530
Autor:
Ye, Zihuiwen, Greenlee-Scott, Fraser, Bartolo, Max, Blunsom, Phil, Campos, Jon Ander, Gallé, Matthias
Reward models (RMs) play a critical role in aligning language models through the process of reinforcement learning from human feedback. RMs are trained to predict a score reflecting human preference, which requires significant time and cost for human
Externí odkaz:
http://arxiv.org/abs/2405.20850
Autor:
Aryabumi, Viraat, Dang, John, Talupuru, Dwarak, Dash, Saurabh, Cairuz, David, Lin, Hangyu, Venkitesh, Bharat, Smith, Madeline, Campos, Jon Ander, Tan, Yi Chern, Marchisio, Kelly, Bartolo, Max, Ruder, Sebastian, Locatelli, Acyr, Kreutzer, Julia, Frosst, Nick, Gomez, Aidan, Blunsom, Phil, Fadaee, Marzieh, Üstün, Ahmet, Hooker, Sara
This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (\"Ust\"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya
Externí odkaz:
http://arxiv.org/abs/2405.15032
In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context is required to answer a given question. Given the performance of IR sy
Externí odkaz:
http://arxiv.org/abs/2404.19705
Autor:
Sainz, Oscar, Campos, Jon Ander, García-Ferrero, Iker, Etxaniz, Julen, de Lacalle, Oier Lopez, Agirre, Eneko
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamination happens when a Large Language Model (LLM) is trained on the test
Externí odkaz:
http://arxiv.org/abs/2310.18018
Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-based string manipulation has been proposed as an alternative, but thei
Externí odkaz:
http://arxiv.org/abs/2310.09350
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER sys
Externí odkaz:
http://arxiv.org/abs/2304.10637
Autor:
Scheurer, Jérémy, Campos, Jon Ander, Korbak, Tomasz, Chan, Jun Shern, Chen, Angelica, Cho, Kyunghyun, Perez, Ethan
Pretrained language models often generate outputs that are not in line with human preferences, such as harmful text or factually incorrect summaries. Recent work approaches the above issues by learning from a simple form of human feedback: comparison
Externí odkaz:
http://arxiv.org/abs/2303.16755
Autor:
Chen, Angelica, Scheurer, Jérémy, Korbak, Tomasz, Campos, Jon Ander, Chan, Jun Shern, Bowman, Samuel R., Cho, Kyunghyun, Perez, Ethan
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedb
Externí odkaz:
http://arxiv.org/abs/2303.16749
Autor:
Scheurer, Jérémy, Campos, Jon Ander, Chan, Jun Shern, Chen, Angelica, Cho, Kyunghyun, Perez, Ethan
Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human eval
Externí odkaz:
http://arxiv.org/abs/2204.14146