Publications

Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Guillaume Dumas
Guillaume Lajoie
Jonathan Simon
Yoshua Bengio
2024-03-01
Neuroscience of Consciousness (publié)
doi.org
arxiv.org
The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent
Julia Ayache
Guillaume Dumas
Alexander Sumich
D. Kuss
Darren Rhodes
Nadja Heym
2024-03-01
Personality and Individual Differences (publié)
doi.org
COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin
Philippe Formont
Jackie Chi Kit Cheung
Pablo Piantanida
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (voir plus)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
Crowdkeeping in Last-mile Delivery
Xin Wang
Okan Arslan
Érick Delage
2024-02-29
Transportation Science (publié)
doi.org
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
Hado van Hasselt
Razvan Pascanu
James Martens
Will Dabney
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (voir 46 de plus)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov
Raymond Li
Loubna Ben allal
Federico Cassano
Joel Lamy-Poirier
Nouamane Tazi
Ao Tang
Dmytro Pykhtar
Jiawei Liu
Yuxiang Wei
Tianyang Liu
Max Tian
Denis Kocetkov
Arthur Zucker
Younes Belkada
Zijian Wang
Qian Liu
Dmitry Abulkhanov
Indraneil Paul
Zhuang Li … (voir 46 de plus)
Wen-Ding Li
Megan L. Risdal
Jia LI
Jian Zhu
Terry Yue Zhuo
Evgenii Zheltonozhskii
Nii Osae Osae Dade
Wenhao Yu
Lucas Krauss
Naman Jain
Yixuan Su
Xuanli He
Manan Dey
Edoardo Abati
Yekun Chai
Niklas Muennighoff
Xiangru Tang
Muhtasham Oblokulov
Christopher Akiki
Marc Marone
Chenghao Mou
Mayank Mishra
Alex Gu
Binyuan Hui
Tri Dao
Armel Zebaze
Olivier Dehaene
Nicolas Patry
Canwen Xu
Julian McAuley
Han Hu
Torsten Scholak
Sebastien Paquet
Jennifer Robinson
Carolyn Jane Anderson
Nicolas Chapados
Md. Mostofa Ali Patwary
Nima Tajbakhsh
Yacine Jernite
Carlos Muñoz Ferrandis
Lingming Zhang
Sean Hughes
Thomas Wolf
Arjun Guha
Leandro Von Werra
Harm de Vries
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
The use of dose surface maps as a tool to investigate spatial dose delivery accuracy for the rectum during prostate radiotherapy
Haley Patrick
John Kildea
2024-02-29
Journal of Applied Clinical Medical Physics (publié)
doi.org
When does word order matter and when doesn't it?
Xuanda Chen
Timothy John O'donnell
Siva Reddy
Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose … (voir plus)that linguistic redundancy can explain this phenomenon, whereby word order and other linguistic cues such as case markers provide overlapping and thus redundant information. Our hypothesis is that models exhibit insensitivity to word order when the order provides redundant information, and the degree of insensitivity varies across tasks. We quantify how informative word order is using mutual information (MI) between unscrambled and scrambled sentences. Our results show the effect that the less informative word order is, the more consistent the model's predictions are between unscrambled and scrambled sentences. We also find that the effect varies across tasks: for some tasks, like SST-2, LMs' prediction is almost always consistent with the original one even if the Pointwise-MI (PMI) changes, while for others, like RTE, the consistency is near random when the PMI gets lower, i.e., word order is really important.
2024-02-29
ArXiv (prépublication)
doi.org
arxiv.org
Acoustic tactile sensing for mobile robot wheels
Wilfred Mason
David Brenken
Falcon Z. Dai
Ricardo Gonzalo Cruz Castillo
Olivier St-Martin Cormier
Audrey Sedal
2024-02-28
ArXiv (prépublication)
doi.org
arxiv.org
ICE-SEARCH: A Language Model-Driven Feature Selection Approach
Tianze Yang
Tianyi Yang
Shaoshan Liu
Fuyuan Lyu
Xue (Steve) Liu
This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, the first work that melds language models (LMs) with evolutionary… (voir plus) algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. Our results not only demonstrate the efficacy of ICE-SEARCH in medical FS but also underscore the versatility, efficiency, and scalability of integrating LMs in FS tasks. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and swift convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.
2024-02-28
ArXiv (prépublication)
doi.org
arxiv.org
On the Challenges and Opportunities in Generative AI
Laura Manduchi
Kushagra Pandey
Robert Bamler
Ryan Cotterell
Sina Daubener
Sophie Fellenz
Asja Fischer
Thomas Gartner
Matthias Kirchler
Marius Kloft
Yingzhen Li
Christoph Lippert
Gerard de Melo
Eric T. Nalisnick
Bjorn Ommer
Rajesh Ranganath
Maja Rudolph
Karen Ullrich
Guy Van den Broeck
Julia E Vogt … (voir 5 de plus)
Yixin Wang
Florian Wenzel
Frank Wood
Stephan Mandt
Vincent Fortuin
2024-02-28
ArXiv (prépublication)
doi.org
arxiv.org