Contextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification Tasks (2023)

Abstract: This paper demonstrates how the limitations of pre-trained models and open evaluation datasets factor into assessing the performance of binary semantic similarity classification tasks. As (1) end-user-facing documentation around the curation of these datasets and pre-trained model training regimes is often not easily accessible and (2) given the lower friction and higher demand to quickly deploy such systems in real-world contexts, our study reinforces prior work showing performance disparities across datasets, embedding techniques and distance metrics, while highlighting the importance of understanding how data is collected, curated and analyzed in semantic similarity classification.

## introduction with the recent popularization of transformer-based large language models (llms) @xcite there has been a renewed interest in text embeddings (learned vector representations of words or sentences) for applications including search, recommendations and semantic similarity ranking or classification. at the same time, platforms like tensorflow hub, huggingface and kaggle have democratized access to a preponderance of pretrained models tuned for a variety of tasks. in both cases documentation regarding the training, tuning and limits of models is often incomplete or difficult to find / consume, with an increased incentive and velocity towards quick deployment of these systems in real-world contexts. in this paper we investigate the performance of a binary semantic similarity classification task to demonstrate these challenges. though a strict formalization of the term semantic similarity is an open problem across a number of disciplines, we use the term in its colloquial machine learning sense: as the degree of likeness in meaning between texts, rather than the more general concept of semantic relatedness, which includes lexical relationships that may drastically alter the likeness in meaning @xcite . we perform this study using a variety of accessible pre-trained models on 3 commonly used evaluation datasets, reflecting on the provenance, characteristics and limitations of both models and datasets as documented and measured by the original authors or related works. we consider the impact of these factors, as well as the choice of distance metric on the classification task. in doing so we hope to reinforce that there exist notable differences in model performance across these datasets within the context of the ethical and architectural considerations of the artifacts themselves. ## ethical considerations as we discuss in the datasets, models and embeddings sections of this paper, both the evaluation datasets we use, as well as the pretrained language models' training data skew heavily english (in some cases by design) and western (in terms of the web platforms from which data was originally sourced, along with the user demographics of said platforms). further, given historical gender divides in access to internet, mobile and digital technologies, as well as cultural barriers faced by women (particularly in education and labor participation) (oecd, 2018) we presume a meaningful male bias in these corpus' web content. as we use these datasets to evaluate semantic similarity classification on associated english text embeddings produced by these pretrained models, we therefore expect our study results to be largely relevant only to contexts that conform to these demographics. in part, our work in this paper to understand the origin, curation methods and contours of datasets and models serves to highlight the need to constrain conclusions about machine learning task performance based on the limits of their data as much as their architectures, objectives @xcite . as a benchmark on the accessibility of such disclosures we selected huggingface as an exemplar for both ease-of-use and their focus on making model cards both easier for developers to produce, and easier for end-users to consume @xcite . we drew four samples (@xmath0, 2023: both for model and data cards, both download-weighted and uniformly, and calculated a binomial confidence interval at α = 0.05 to estimate m the prevalence of model cards (conservatively, as the existence of a readme file as per the huggingface model card guide) and k the prevalence of model cards with terms associated with fairness, citations, annotators or limitations (see in all, these observations motivate caution in the deployment of such systems without increased due diligence -especially in consequential domainsgiven the potential for disparate impact on users from out-of-distribution groups. ## results & conclusions overall, all-mpnet-base-v2 proved robust across all 3 study datasets, with the best auc on mrpc and stsb similarity classification (0.7125 and 0.8375 respectively). while gpt-4 was the best performing model on qqp dataset (0.8177), it's auc was also the most unstable across datasets, with a 0.1834 point spread between stsb and mrpc. though the choice of distance metric produced marginal differences in score, the effect of model choice and dataset were more pronounced. models generally performed better on stsb with mean au @xmath1.7837 across metrics and models, and the worst on mrpc at mean au @xmath2.6981 (see figure 4 ). together these results align with the observations regarding dataset and ground truth curation in the datasets section. specifically, the higher complexity of sentences in mrpc, along with measured inter-rater agreement of only 85% may go towards explaining why our study showed the worst performance across models on this dataset. at the same time, both stsb and qqp sentences displayed more structural simplicity, and while there is some documented inter-rater disagreement in at least one of these, this may explain generally better study performance across both. further, given the limits of the study (englishonly), as well as the model and dataset content, ground truth labels, and labeling processes discussed in the ethical considerations, datasets and models sections, caution should be taken in the deployment of these pretrained models / evaluation criteria for semantic similarity classification in real-world contexts.