no code implementations • 1 Apr 2024 • Yi-Lin Tuan, Xilun Chen, Eric Michael Smith, Louis Martin, Soumya Batra, Asli Celikyilmaz, William Yang Wang, Daniel M. Bikel
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience.
1 code implementation • 27 Sep 2023 • Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, Hao Ma
We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.
2 code implementations • 24 Aug 2023 • Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, Gabriel Synnaeve
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #27 on Code Generation on MBPP
14 code implementations • 18 Jul 2023 • Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on Question Answering on PubChemQA
1 code implementation • 15 Jun 2023 • Mikhail Plekhanov, Nora Kassner, Kashyap Popat, Louis Martin, Simone Merello, Borislav Kozlovskii, Frédéric A. Dreyer, Nicola Cancedda
Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
no code implementations • 19 May 2023 • Mattia Atzeni, Mikhail Plekhanov, Frédéric A. Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
1 code implementation • 13 Sep 2022 • Christina Du, Kashyap Popat, Louis Martin, Fabio Petroni
Detection and disambiguation of all entities in text is a crucial task for a wide range of applications.
no code implementations • 20 Dec 2021 • Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.
2 code implementations • 15 Apr 2021 • Thomas Scialom, Louis Martin, Jacopo Staiano, Éric Villemonte de la Clergerie, Benoît Sagot
In the context of Sentence Simplification, this is particularly challenging: the task requires by nature to replace complex words with simpler ones that shares the same meaning.
no code implementations • JEPTALNRECITAL 2020 • Louis Martin, Benjamin Muller, Pedro Javier Ortiz Su{\'a}rez, Yoann Dupont, Laurent Romary, {\'E}ric Villemonte de la Clergerie, Beno{\^\i}t Sagot, Djam{\'e} Seddah
L{'}utilisation pratique de ces mod{\`e}les {---} dans toutes les langues sauf l{'}anglais {---} {\'e}tait donc limit{\'e}e. La sortie r{\'e}cente de plusieurs mod{\`e}les monolingues fond{\'e}s sur BERT (Devlin et al., 2019), notamment pour le fran{\c{c}}ais, a d{\'e}montr{\'e} l{'}int{\'e}r{\^e}t de ces mod{\`e}les en am{\'e}liorant l{'}{\'e}tat de l{'}art pour toutes les t{\^a}ches {\'e}valu{\'e}es.
1 code implementation • ACL 2020 • Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, Lucia Specia
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
1 code implementation • LREC 2022 • Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, Benoît Sagot
Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English.
Ranked #2 on Text Simplification on ASSET
6 code implementations • ACL 2020 • Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, Benoît Sagot
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on Dependency Parsing on French GSD
2 code implementations • LREC 2020 • Louis Martin, Benoît Sagot, Éric de la Clergerie, Antoine Bordes
Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical.
Ranked #3 on Text Simplification on ASSET
1 code implementation • IJCNLP 2019 • Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems.
1 code implementation • WS 2018 • Louis Martin, Samuel Humeau, Pierre-Emmanuel Mazaré, Antoine Bordes, Éric Villemonte de la Clergerie, Benoît Sagot
We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
no code implementations • CONLL 2018 • Ganesh Jawahar, Benjamin Muller, Amal Fethi, Louis Martin, {\'E}ric Villemonte de la Clergerie, Beno{\^\i}t Sagot, Djam{\'e} Seddah
We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set.