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20232025
most citedLarge Concept Models: Language Modeling in a Sentence Representation Space

10 citations · 10 across the 3 of their papers we have counts for

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cs.CL2025

BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation

The Omnilingual MT Team, Pierre Andrews, Mikel Artetxe +14

BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages…

cs.CL2024

2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset

Marta R. Costa-jussà, Bokai Yu, Pierre Andrews +7

We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 74 spoken languages at the inters…

cs.CL202410 cited

Large Concept Models: Language Modeling in a Sentence Representation Space

LCM team, Loïc Barrault, Paul-Ambroise Duquenne +18

LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input a…

cs.CL2024

LCFO: Long Context and Long Form Output Dataset and Benchmarking

Marta R. Costa-jussà, Pierre Andrews, Mariano Coria Meglioli +10

This paper presents the Long Context and Form Output (LCFO) benchmark, a novel evaluation framework for assessing gradual summarization and summary expansion capabilities across di…

cs.CL2024

Towards Red Teaming in Multimodal and Multilingual Translation

Christophe Ropers, David Dale, Prangthip Hansanti +9

Assessing performance in Natural Language Processing is becoming increasingly complex. One particular challenge is the potential for evaluation datasets to overlap with training da…

cs.CL2023

Seamless: Multilingual Expressive and Streaming Speech Translation

Seamless Communication, Loïc Barrault, Yu-An Chung +62

Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this wo…