Are LLMs Better than Reported? Detecting Label Errors and Mitigating
  Their Effect on Model Performance
Authors: name: Omer Nahum, name: Nitay Calderon, name: Orgad Keller, name: Idan Szpektor, name: Roi Reichart

NLP benchmarks rely on standardized datasets for training and evaluating
models and are crucial for advancing the field. Traditionally, expert
annotations ensure high-quality labels; however, the cost of expert annotation
does not scale well with the growing demand for larger datasets required by
modern models. While crowd-sourcing provides a more scalable solution, it often
comes at the expense of annotation precision and consistency. Recent
advancements in large language models (LLMs) offer new opportunities to enhance
the annotation process, particularly for detecting label errors in existing
datasets. In this work, we consider the recent approach of LLM-as-a-judge,
leveraging an ensemble of LLMs to flag potentially mislabeled examples. Through
a case study of four datasets from the TRUE benchmark, covering different tasks
and domains, we empirically analyze the labeling quality of existing datasets,
and compare expert, crowd-sourced, and our LLM-based annotations in terms of
agreement, label quality, and efficiency, demonstrating the strengths and
limitations of each annotation method. Our findings reveal a substantial number
of label errors, which, when corrected, induce a significant upward shift in
reported model performance. This suggests that many of the LLMs so-called
mistakes are due to label errors rather than genuine model failures.
Additionally, we discuss the implications of mislabeled data and propose
methods to mitigate them in training to improve model performance.