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20242026
most citedMultilingual Retrieval-Augmented Generation for Knowledge-Intensive Task

4 citations · 7 across the 20 of their papers we have counts for

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cs.CL2026

Adam's Law: Textual Frequency Law on Large Language Models

Hongyuan Adam Lu, Z. L., Victor Wei +5

While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…

cs.CL2026

EuroLLM-22B: Technical Report

Miguel Moura Ramos, Duarte M. Alves, Hippolyte Gisserot-Boukhlef +15

This report presents EuroLLM-22B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11…

cs.CL2025

HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models

Stephan Oepen, Nikolay Arefev, Mikko Aulamo +29

We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely th…

cs.CL2025

DocHPLT: A Massively Multilingual Document-Level Translation Dataset

Dayyán O'Brien, Bhavitvya Malik, Ona de Gibert +3

Existing document-level machine translation resources are only available for a handful of languages, mostly high-resourced ones. To facilitate the training and evaluation of docume…

cs.CL2025

MGen: Millions of Naturally Occurring Generics in Context

Gustavo Cilleruelo, Emily Allaway, Barry Haddow +1

MGen is a dataset of over 4 million naturally occurring generic and quantified sentences extracted from diverse textual sources. Sentences in the dataset have long context document…

cs.CL2025

Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization

Weixuan Wang, Minghao Wu, Barry Haddow +1

Long document summarization remains a significant challenge for current large language models (LLMs), as existing approaches commonly struggle with information loss, factual incons…