Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information (2110.08420v2)

Abstract: Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. However, this comparison provides little understanding of how difficult each instance in a given distribution is, or what attributes make the dataset difficult for a given model. To address these questions, we frame dataset difficulty -- w.r.t. a model $\mathcal{V}$ -- as the lack of $\mathcal{V}$-$\textit{usable information}$ (Xu et al., 2019), where a lower value indicates a more difficult dataset for $\mathcal{V}$. We further introduce $\textit{pointwise $\mathcal{V}$-information}$ (PVI) for measuring the difficulty of individual instances w.r.t. a given distribution. While standard evaluation metrics typically only compare different models for the same dataset, $\mathcal{V}$-$\textit{usable information}$ and PVI also permit the converse: for a given model $\mathcal{V}$, we can compare different datasets, as well as different instances/slices of the same dataset. Furthermore, our framework allows for the interpretability of different input attributes via transformations of the input, which we use to discover annotation artefacts in widely-used NLP benchmarks.

• The paper introduces V-usable information to quantify dataset difficulty relative to a model's prediction capabilities.
• It extends the framework with pointwise V-information (pvi) to assess individual instance difficulty within datasets.
• Application of this metric uncovers latent biases and performance divergences across datasets like SNLI and MultiNLI.

Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information

This paper by Ethayarajh, Choi, and Swayamdipta presents a nuanced approach to assessing the difficulty of datasets in the context of machine learning, specifically through the lens of $\mathcal{V}$-usable information. The authors introduce $\mathcal{V}$-usable information as a metric to determine the dataset's difficulty relative to a model, $\mathcal{V}$. The fundamental thesis is that a dataset's difficulty can be framed as the lack of usable information with respect to the computational constraints and capabilities of a given model.

Key Contributions

1. Conceptualization of Dataset Difficulty:
   • Traditional practices measure dataset difficulty by comparing model performance to human performance. However, this does not offer insights into the difficulty of individual data instances within a dataset.
   • The paper proposes framing dataset difficulty as a deficiency in usable information, defined in terms of a specific model $\mathcal{V}$'s ability to make predictions.
2. Pointwise $\mathcal{V}$-Information (pvi):
   • The authors extend their framework to introduce pvi, which assesses individual instance difficulty. Pvi measures how much information each instance provides towards accurate prediction by the model, offering granular insights beyond aggregate dataset evaluations.
3. Application of $\mathcal{V}$-Usable Information:
   • This metric is applied across various datasets to show its efficacy in comparing dataset difficulties for the same task but different datasets.
   • Analysis reveals notable divergences in the difficulty level presented by different datasets to the same neural model, corroborating empirical observations in NLP benchmarks like SNLI and MultiNLI.

Insights and Implications

• Interpreting Dataset Artefacts:
  • The authors employ $\mathcal{V}$-usable information to elucidate why certain datasets are more or less challenging for specific models. This analysis reveals latent biases and annotation artefacts.
  • For instance, in the SNLI dataset, the hypothesis alone (excluding the premise) can contain significant information, highlighting common annotator biases exploited by models.
• Practical Implications for NLP Tasks:
  • Demonstrating the utility of this framework for debugging datasets and improving model interpretability, the paper uncovers surprising biases in hate speech datasets that could lead to misleading model performance.
• Methodological Utility:
  • The framework enriches the interpretative capacity of both dataset developers and model users, offering a structured method for contemplating dataset improvements in benchmarking and model training.

Future Directions

The authors suggest several avenues for future research, including extending this framework to other data modalities beyond textual data, such as images or audio, and further refining the concept to accommodate open-ended text generation tasks. Such advancements could enhance model evaluation accuracy, dataset curation, and could inform the development of more robust machine learning models.

In conclusion, this paper introduces a theoretically grounded, empirically validated framework for understanding dataset difficulty through $\mathcal{V}$-usable information. This approach offers a significant step towards more meaningful dataset evaluations and model interpretability in AI, particularly within NLP.