NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, Eneko Agirre

11-02-2023 Natural Language Processing, Large Language Models, Evaluation, Data Contamination, Deep Learning

Resumen

In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble. The worst kind of data contamination happens when a Large Language Model (LLM) is trained on the test split of a benchmark, and then evaluated in the same benchmark. The extent of the problem is unknown, as it is not straightforward to measure. Contamination causes an overestimation of the performance of a contaminated model in a target benchmark and associated task with respect to their non-contaminated counterparts. The consequences can be very harmful, with wrong scientific conclusions being published while other correct ones are discarded. This position paper defines different levels of data contamination and argues for a community effort, including the development of automatic and semi-automatic measures to detect when data from a benchmark was exposed to a model, and suggestions for flagging papers with conclusions that are compromised by data contamination.

Tipo

Artículo de revista

Publicación

EMNLP 2023 Findings

Natural Language Processing Large Language Models Evaluation Data Contamination Deep Learning

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Resumen

Julen Etxaniz

Estudiante de Doctorado en Análisis y Procesamiento del Lenguaje