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Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models

Paper · 2020 · Viktor Schlegel, Goran Nenadic and Riza Batista-Navarro

Summary

High leaderboard scores on Natural Language Inference tasks can hide the fact that models succeed by exploiting shallow regularities in the data rather than by reasoning. This survey maps that problem for English. It treats NLI broadly, covering both textual entailment (RTE) and machine reading comprehension (MRC). The authors started from three influential seed papers and followed citations in both directions ("snowballing"), collecting 69 studies. They sorted these by the weakness reported and by the method used to expose or reduce it. The weaknesses fall into three groups: spurious correlations in datasets, design flaws that make a dataset unfit for testing a capability, and plain quality problems. Detection methods include partial-input baselines, adversarial evaluation sets and stress-tests. Remedies include augmentation, external knowledge, and changes to architecture or training. A separate pass over dataset papers found that many recent resources were never checked for such artefacts. The survey ends with research recommendations. Its value is as a toolbox for anyone who builds a dataset or claims that a model has gained a capability.

Key points

  • The corpus holds 69 papers: 6 from 2014–2017, 17 from 2018, 38 from 2019 and 8 from 2020. Most (48) appeared at ACL-hosted venues, 5 at AAAI, 3 at ICLR, 3 elsewhere, and 10 only on arXiv.
  • The search began from three starting papers: Jia and Liang (2017), along with the 2018 works by Poliak et al. and by Gururangan et al. The search excluded work before 2014, non-English datasets, adversarial-attack research and fairness research.
  • More than half of the surveyed studies target RTE. Known artefacts include:
    • hypothesis n-grams and sentence length predicting the label;
    • word overlap between premise and hypothesis predicting entailment in MNLI (McCoy et al., 2019);
    • exploitable cues in the endings of ROC Stories, plus similar issues in COPA and ARCT.
  • About half of the datasets examined (36 of 75) have no published analysis of possible spurious correlations. Adversarial pruning of new datasets against neural models is becoming more common (eight cases across 2018–2019).
  • Model difficulty and human difficulty do not line up:
    • Humans did better on a cue-free "hard" subset than on the biased data (Yu et al., 2020).
    • Model confidence does not follow inter-annotator agreement (Pavlick and Kwiatkowski, 2019).
    • Models built on syntax trees resisted syntactic adversaries better (McCoy et al., 2019).

Interesting ideas

  • I1. Partial-input baselines. Train a model on only part of the input, such as the hypothesis alone. This is a cheap way to estimate how much of a benchmark can be solved without real inference; a high score signals artefacts, though a low score does not prove they are absent.
  • I2. Inserting distracting sentences. Add a sentence that shares many words with the question but does not answer it, as in Jia and Liang. This shows whether a system finds answers by word matching instead of by meaning.
  • I3. Separating architecture from trained model. Keep apart failures caused by the training data, which vary with the data, and failures inherent to the model class, which persist across datasets. Comparing the same architecture trained on different data, and different architectures on the same data, shows where an error really comes from.
  • I4. Overlap-heuristic stress sets. Build controlled examples where high word overlap between two sentences does not imply entailment, such as swapped arguments or subsequences, to test whether a system tracks structure.
  • I5. Comparing model confidence with human agreement. Check whether a system's confidence follows how strongly human annotators agree, as a way to judge whether its uncertainty is meaningful.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Add "question-only" and "text-withheld" runs of the readers as negative controls, so any test that these runs solve is flagged as leaking its answer.
  • I2 (answer): Automatically add near-miss sentences to fables and school texts and check that answers still come from the correct entity in the world model, not from lexical overlap.
  • I3 (tree): Cross-train the perceptron parser and gparse on different treebanks and UD dialect conversions to decide whether a recurring attachment error comes from the data or from the parser design.
  • I4 (frames): Make minimal pairs that differ only in who did what to whom and check that the VerbNet role frames change accordingly, even though the bag of words stays identical.
  • I5 (learn): Test whether agreement among witness parsers on silver data predicts where gold or teacher-tier labels disagree, and use any mismatch to recalibrate which silver trees are trusted.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 3Research subjects and languages the work is about.English · 1,373 worksReasoning · 510 worksEvaluation & benchmarks · 685 works

Cite

Cite as Viktor Schlegel, Goran Nenadic and Riza Batista-Navarro. 2020. Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models. arXiv:2005.14709.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2005.14709 · arXiv 2005.14709

@misc{schlegel2020beyond,
  title = {{Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models}},
  author = {Viktor Schlegel, Goran Nenadic and Riza Batista-Navarro},
  year = {2020},
  doi = {10.48550/arxiv.2005.14709},
  eprint = {2005.14709},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2005.14709}
}

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