TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
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Computer Science > Computation and Language
Title:TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
Abstract:Applied Behavior Analysis (ABA) is a clinical discipline whose documentation, teaching programs and multi-session behavioral logs, is formulaic and high-volume, yet real session data is HIPAA-protected and bound by professional confidentiality rules, blocking the release of a training corpus. We present TRACE (Taxonomy-Referenced ABA Clinical Examples), a 2,999-example synthetic instruction-tuning dataset covering two ABA tasks: teaching-program generation across Discrete Trial Training, Natural Environment Teaching, and Task Analysis; and multi-session behavioral interpretation across twelve trajectory patterns and thirteen target behaviors. Every example is produced by a deterministic taxonomy-driven generator grounded in the canonical ABA literature, and every example carries complete sampling provenance, the exact taxonomy cells that produced it. The dataset is released under CC BY-NC 4.0 for data and MIT for code, with stratified train (2,549), validation (149), test (281), and sanity (20) splits. TRACE is a research artifact and has not been clinically validated.
| Comments: | 11 pages, 3 tables. Dataset: this https URL ; code: this https URL |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE) |
| ACM classes: | I.2.7; I.2.6; J.3 |
| Cite as: | arXiv:2605.25038 [cs.CL] |
| (or arXiv:2605.25038v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25038
arXiv-issued DOI via DataCite (pending registration)
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