Theo dõi
Max Bartolo
Max Bartolo
University College London, Research @CohereAI
Email được xác minh tại cs.ucl.ac.uk - Trang chủ
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Y Lu, M Bartolo, A Moore, S Riedel, P Stenetorp
arXiv preprint arXiv:2104.08786, 2021
10812021
Dynabench: Rethinking benchmarking in NLP
D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ...
arXiv preprint arXiv:2104.14337, 2021
4302021
Winoground: Probing vision and language models for visio-linguistic compositionality
T Thrush, R Jiang, M Bartolo, A Singh, A Williams, D Kiela, C Ross
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
4042022
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
M Bartolo, A Roberts, J Welbl, S Riedel, P Stenetorp
arXiv preprint arXiv:2002.00293, 2020
1822020
Interpretation of Natural Language Rules in Conversational Machine Reading
M Saeidi, M Bartolo, P Lewis, S Singh, T Rocktäschel, M Sheldon, ...
arXiv preprint arXiv:1809.01494, 2018
1822018
Dataperf: Benchmarks for data-centric ai development
M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ...
Advances in Neural Information Processing Systems 36, 5320-5347, 2023
1372023
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
M Bartolo, T Thrush, R Jia, S Riedel, P Stenetorp, D Kiela
arXiv preprint arXiv:2104.08678, 2021
1012021
Aya dataset: An open-access collection for multilingual instruction tuning
S Singh, F Vargus, D Dsouza, BF Karlsson, A Mahendiran, WY Ko, ...
arXiv preprint arXiv:2402.06619, 2024
812024
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
HR Kirk, A Whitefield, P Röttger, A Bean, K Margatina, J Ciro, R Mosquera, ...
arXiv preprint arXiv:2404.16019, 2024
752024
Aya 23: Open weight releases to further multilingual progress
V Aryabumi, J Dang, D Talupuru, S Dash, D Cairuz, H Lin, B Venkitesh, ...
arXiv preprint arXiv:2405.15032, 2024
672024
Human feedback is not gold standard
T Hosking, P Blunsom, M Bartolo
arXiv preprint arXiv:2309.16349, 2023
51*2023
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
M Bartolo, T Thrush, S Riedel, P Stenetorp, R Jia, D Kiela
arXiv preprint arXiv:2112.09062, 2021
372021
Introducing v0. 5 of the ai safety benchmark from mlcommons
B Vidgen, A Agrawal, AM Ahmed, V Akinwande, N Al-Nuaimi, N Alfaraj, ...
arXiv preprint arXiv:2404.12241, 2024
342024
Adversarial nibbler: A data-centric challenge for improving the safety of text-to-image models
A Parrish, HR Kirk, J Quaye, C Rastogi, M Bartolo, O Inel, J Ciro, ...
arXiv preprint arXiv:2305.14384, 2023
22*2023
Dynatask: A framework for creating dynamic AI benchmark tasks
T Thrush, K Tirumala, A Gupta, M Bartolo, P Rodriguez, T Kane, ...
arXiv preprint arXiv:2204.01906, 2022
172022
Fishing for magikarp: Automatically detecting under-trained tokens in large language models
S Land, M Bartolo
arXiv preprint arXiv:2405.05417, 2024
152024
Undersensitivity in Neural Reading Comprehension
J Welbl, P Minervini, M Bartolo, P Stenetorp, S Riedel
arXiv preprint arXiv:2003.04808, 2020
152020
DMLR: Data-centric Machine Learning Research--Past, Present and Future
L Oala, M Maskey, L Bat-Leah, A Parrish, NM Gürel, TS Kuo, Y Liu, R Dror, ...
arXiv preprint arXiv:2311.13028, 2023
132023
Contrasting human-and machine-generated word-level adversarial examples for text classification
M Mozes, M Bartolo, P Stenetorp, B Kleinberg, LD Griffin
arXiv preprint arXiv:2109.04385, 2021
112021
Improving reward models with synthetic critiques
Z Ye, F Greenlee-Scott, M Bartolo, P Blunsom, JA Campos, M Gallé
arXiv preprint arXiv:2405.20850, 2024
82024
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