How not to Lie with a Benchmark: Rearranging NLP Leaderboards

• URL: http://arxiv.org/abs/2112.01342v1
• Date: Thu, 2 Dec 2021 15:40:52 GMT
• Title: How not to Lie with a Benchmark: Rearranging NLP Leaderboards
• Authors: Shavrina Tatiana and Malykh Valentin
• Abstract summary: We examine popular NLP benchmarks' overall scoring methods and rearrange the models by geometric and harmonic mean. We analyze several popular benchmarks including GLUE, SuperGLUE, XGLUE, and XTREME.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comparison with a human is an essential requirement for a benchmark for it to be a reliable measurement of model capabilities. Nevertheless, the methods for model comparison could have a fundamental flaw - the arithmetic mean of separate metrics is used for all tasks of different complexity, different size of test and training sets. In this paper, we examine popular NLP benchmarks' overall scoring methods and rearrange the models by geometric and harmonic mean (appropriate for averaging rates) according to their reported results. We analyze several popular benchmarks including GLUE, SuperGLUE, XGLUE, and XTREME. The analysis shows that e.g. human level on SuperGLUE is still not reached, and there is still room for improvement for the current models.

Related papers

• Data Efficient Evaluation of Large Language Models and Text-to-Image Models via Adaptive Sampling [3.7467864495337624]
  SubLIME is a data-efficient evaluation framework for text-to-image models. Our approach ensures statistically aligned model rankings compared to full datasets. We leverage the HEIM leaderboard to cover 25 text-to-image models on 17 different benchmarks.
  arXiv  Detail & Related papers  (2024-06-21T07:38:55Z)
• Diversity-Aware Ensembling of Language Models Based on Topological Data Analysis [3.1734682813501514]
  Existing approaches mostly rely on simple averaging of predictions by ensembles with equal weights for each model. We propose to estimate weights for ensembles of NLP models using not only knowledge of their individual performance but also their similarity to each other.
  arXiv  Detail & Related papers  (2024-02-22T00:04:21Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Anchor Points: Benchmarking Models with Much Fewer Examples [88.02417913161356]
  In six popular language classification benchmarks, model confidence in the correct class on many pairs of points is strongly correlated across models. We propose Anchor Point Selection, a technique to select small subsets of datasets that capture model behavior across the entire dataset. Just several anchor points can be used to estimate model per-class predictions on all other points in a dataset with low mean absolute error.
  arXiv  Detail & Related papers  (2023-09-14T17:45:51Z)
• Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation [55.92852268168816]
  N-gram matching-based evaluation metrics, such as BLEU and chrF, are widely utilized across a range of natural language generation (NLG) tasks. Recent studies have revealed a weak correlation between these matching-based metrics and human evaluations. We propose to utilize textitmultiple references to enhance the consistency between these metrics and human evaluations.
  arXiv  Detail & Related papers  (2023-08-06T14:49:26Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Comparing Foundation Models using Data Kernels [13.099029073152257]
  We present a methodology for directly comparing the embedding space geometry of foundation models. Our methodology is grounded in random graph theory and enables valid hypothesis testing of embedding similarity. We show how our framework can induce a manifold of models equipped with a distance function that correlates strongly with several downstream metrics.
  arXiv  Detail & Related papers  (2023-05-09T02:01:07Z)
• Evaluation of HTR models without Ground Truth Material [2.4792948967354236]
  evaluation of Handwritten Text Recognition models during their development is straightforward. But the evaluation process becomes tricky as soon as we switch from development to application. We show that lexicon-based evaluation can compete with lexicon-based methods.
  arXiv  Detail & Related papers  (2022-01-17T01:26:09Z)
• Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays [62.997667081978825]
  Generalized Linear Latent Variable models (GLLVMs) generalize such factor models to non-Gaussian responses. Current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets. We propose a new approach for fitting GLLVMs to high-dimensional datasets, based on approximating the model using penalized quasi-likelihood.
  arXiv  Detail & Related papers  (2020-10-06T04:28:19Z)
• Interpretable Meta-Measure for Model Performance [4.91155110560629]
  We introduce a new meta-score assessment named Elo-based Predictive Power (EPP) EPP is built on top of other performance measures and allows for interpretable comparisons of models. We prove the mathematical properties of EPP and support them with empirical results of a large scale benchmark on 30 classification data sets and a real-world benchmark for visual data.
  arXiv  Detail & Related papers  (2020-06-02T14:10:13Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.