4 accepted papers
Adaptive testing is widely adopted in AI-driven educational assessment systems (e.g., GRE), where the goal is to select an optimal subset of questions from a large question pool to accurately estimate an examinee's ability. A fundamental challenge is that: optimal question subsets are inherently per…
As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed various large-scale benchmarks to determine their capabilities, typically against a gold-standard test set and report metrics averaged across all items. Howe…
Computerized adaptive testing (CAT), as a tool that can efficiently measure student's ability, has been widely used in various standardized tests (e.g., GMAT and GRE). The adaptivity of CAT refers to the selection of the most informative questions for each student, reducing test length. Existing CAT…
Work with neural word embeddings and lexical relations has largely focused on confirmatory experiments which use human-curated examples of semantic and syntactic relations to validate against. In this paper, we explore the degree to which lexical relations, such as those found in popular validation…