Evading Data Contamination Detection for Language Models is (too) Easy

Evading Data Contamination Detection for Language Models is (too) Easy

5 February 2024

Jasper Dekoninck

Mark Niklas Muller

Maximilian Baader

Martin Vechev

Papers citing "Evading Data Contamination Detection for Language Models is (too) Easy"

9 / 9 papers shown

Title
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication A. Khan Robert Underwood Carlo Siebenschuh Y. Babuji Aswathy Ajith Kyle Hippe Ozan Gokdemir Alexander Brace Kyle Chard Ian T. Foster 31 0 0 06 Nov 2024
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions Yujuan Fu Özlem Uzuner Meliha Yetisgen Fei Xia 38 3 0 24 Oct 2024
Ward: Provable RAG Dataset Inference via LLM Watermarks Nikola Jovanović Robin Staab Maximilian Baader Martin Vechev 43 1 0 04 Oct 2024
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation Jasper Dekoninck Maximilian Baader Martin Vechev ALM 85 0 0 01 Sep 2024
Task Contamination: Language Models May Not Be Few-Shot Anymore Changmao Li Jeffrey Flanigan 79 87 0 26 Dec 2023
Data Contamination Through the Lens of Time Manley Roberts Himanshu Thakur Christine Herlihy Colin White Samuel Dooley 75 30 0 16 Oct 2023
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 234 447 0 14 Jul 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 236 1,508 0 31 Dec 2020
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 264 1,798 0 14 Dec 2020