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20182026
most citedScaling Up Models and Data with and

48 citations · 62 across the 4 of their papers we have counts for

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cs.CL2026

Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability

Alicia Parrish, Rajat Shinde, Sanket Badhe +57

Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…

cs.CL2024

MiTTenS: A Dataset for Evaluating Gender Mistranslation

Kevin Robinson, Sneha Kudugunta, Romina Stella +2

Translation systems, including foundation models capable of translation, can produce errors that result in gender mistranslation, and such errors can be especially harmful. To meas…

cs.CL2023

Low-Rank Adaptation for Multilingual Summarization: An Empirical Study

Chenxi Whitehouse, Fantine Huot, Jasmijn Bastings +3

Although the advancements of pre-trained Large Language Models have significantly accelerated recent progress in NLP, their ever-increasing size poses significant challenges for co…

cs.CL2023

Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Mor Geva, Jasmijn Bastings, Katja Filippova +1

Transformer-based language models (LMs) are known to capture factual knowledge in their parameters. While previous work looked into where factual associations are stored, only litt…

cs.CL20221 cited

Simple Recurrence Improves Masked Language Models

Tao Lei, Ran Tian, Jasmijn Bastings +1

In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into t…

cs.CL2020

The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?

Jasmijn Bastings, Katja Filippova

There is a recent surge of interest in using attention as explanation of model predictions, with mixed evidence on whether attention can be used as such. While attention convenient…