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Register a new userComparing Test Sets with Item Response Theory
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Clara Vania
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
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Measures of face identification proficiency are essential to ensure accurate and consistent performance by professional forensic face examiners and others who perform face identification tasks in applied scenarios. Current proficiency tests rely on static sets of stimulus items, and so, cannot be administered validly to the same individual multiple times. To create a proficiency test, a large number of items of known difficulty must be assembled. Multiple tests of equal difficulty can be constructed then using subsets of items. Here, we introduce a proficiency test, the Triad Identity Matching (TIM) test, based on stimulus difficulty measures based on Item Response Theory (IRT). Participants view face-image triads (N=225) (two images of one identity and one image of a different identity) and select the different identity. In Experiment 1, university students (N=197) showed wide-ranging accuracy on the TIM test. Furthermore, IRT modeling demonstrated that the TIM test produces items of various difficulty levels. In Experiment 2, IRT-based item difficulty measures were used to partition the TIM test into three equally easy and three equally difficult subsets. Simulation results indicated that the full set, as well as curated subsets, of the TIM items yielded reliable estimates of subject ability. In summary, the TIM test can provide a starting point for developing a framework that is flexible, calibrated, and adaptive to measure proficiency across various ability levels (e.g., professionals or populations with face processing deficits)
Conceptual tests are widely used by physics instructors to assess students conceptual understanding and compare teaching methods. It is common to look at students changes in their answers between a pre-test and a post-test to quantify a transition in students conceptions. This is often done by looking at the proportion of incorrect answers in the pre-test that changes to correct answers in the post-test -- the gain -- and the proportion of correct answers that changes to incorrect answers -- the loss. By comparing theoretical predictions to experimental data on the Force Concept Inventory, we shown that Item Response Theory (IRT) is able to fairly well predict the observed gains and losses. We then use IRT to quantify the students changes in a test-retest situation when no learning occurs and show that $i)$ up to 25% of total answers can change due to the non-deterministic nature of students answer and that $ii)$ gains and losses can go from 0% to 100%. Still using IRT, we highlight the conditions that must satisfy a test in order to minimize gains and losses when no learning occurs. Finally, recommandations on the interpretation of such pre/post-test progression with respect to the initial level of students are proposed.
Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving psychometric modeling leading to improved scientific understanding and public policy. However, while larger datasets allow for more flexible approaches, many contemporary algorithms for fitting IRT models may also have massive computational demands that forbid real-world application. To address this bottleneck, we introduce a variational Bayesian inference algorithm for IRT, and show that it is fast and scalable without sacrificing accuracy. Applying this method to five large-scale item response datasets from cognitive science and education yields higher log likelihoods and higher accuracy in imputing missing data than alternative inference algorithms. Using this new inference approach we then generalize IRT with expressive Bayesian models of responses, leveraging recent advances in deep learning to capture nonlinear item characteristic curves (ICC) with neural networks. Using an eigth-grade mathematics test from TIMSS, we show our nonlinear IRT models can capture interesting asymmetric ICCs. The algorithm implementation is open-source, and easily usable.
The goal of item response theoretic (IRT) models is to provide estimates of latent traits from binary observed indicators and at the same time to learn the item response functions (IRFs) that map from latent trait to observed response. However, in many cases observed behavior can deviate significantly from the parametric assumptions of traditional IRT models. Nonparametric IRT models overcome these challenges by relaxing assumptions about the form of the IRFs, but standard tools are unable to simultaneously estimate flexible IRFs and recover ability estimates for respondents. We propose a Bayesian nonparametric model that solves this problem by placing Gaussian process priors on the latent functions defining the IRFs. This allows us to simultaneously relax assumptions about the shape of the IRFs while preserving the ability to estimate latent traits. This in turn allows us to easily extend the model to further tasks such as active learning. GPIRT therefore provides a simple and intuitive solution to several longstanding problems in the IRT literature.
Item response theory (IRT) has become one of the most popular statistical models for psychometrics, a field of study concerned with the theory and techniques of psychological measurement. The IRT models are latent factor models tailored to the analysis, interpretation, and prediction of individuals behaviors in answering a set of measurement items that typically involve categorical response data. Many important questions of measurement are directly or indirectly answered through the use of IRT models, including scoring individuals test performances, validating a test scale, linking two tests, among others. This paper provides a review of item response theory, including its statistical framework and psychometric applications. We establish connections between item response theory and related topics in statistics, including empirical Bayes, nonparametric methods, matrix completion, regularized estimation, and sequential analysis. Possible future directions of IRT are discussed from the perspective of statistical learning.
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