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20242026
most citedLeak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs

19 citations · 19 across the 4 of their papers we have counts for

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cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +241

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness

Pinzhen Chen, Koel Dutta Chowdhury, Xiaoya Xu +20

Multilingual translation benchmarks are typically sourced in English and translated into other languages, treating language pairs as the unit of evaluation---a design that is prone…

cs.CL2025

Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

Sara Papi, Javier Garcia Gilabert, Zachary Hopton +8

As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which directly process spoken language and enable speech-t…

cs.CL2025

Real-World Summarization: When Evaluation Reaches Its Limits

Patrícia Schmidtová, Ondřej Dušek, Saad Mahamood

We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human…

cs.CL202419 cited

Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs

Simone Balloccu, Patrícia Schmidtová, Mateusz Lango +1

Natural Language Processing (NLP) research is increasingly focusing on the use of Large Language Models (LLMs), with some of the most popular ones being either fully or partially c…