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

Submission Type: Regular Short Paper

Submission Track: Theme Track: Large Language Models and the Future of NLP

Submission Track 2: Resources and Evaluation

Keywords: evaluation, data contamination, large language models, benchmark

TL;DR: The classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble, this paper argues for a community effort to address the issue. .

Abstract:

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.

Submission Number: 3019