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TRACE

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Text Generation
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clinical
applied-behavior-analysis
autism
small-language-model
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instruction-tuning
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TRACE / docs /references.md
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Citations to use — the focused shortlist

Principle: The paper's References section is lean and earned, not a literature dump. This shortlist is the ~30 papers we actually cite across the paper and the dataset documentation (dataset card, datasheet, data statement, taxonomy reference).

A. ABA × NLP — direct predecessors and positioning (6)

  1. Kumar, A., et al. (2024). Personalized-ABA. NLP4Science @ ACL. https://aclanthology.org/2024.nlp4science-1.16/ How we cite: direct predecessor; benchmark to beat.

  2. Cox, D. J., & Jennings, A. M. (2024). Promises and possibilities of AI in behavior analytic services. Behavior Analysis in Practice, 17, 123–136. https://pmc.ncbi.nlm.nih.gov/articles/PMC10890993/ How we cite: motivates the application space, especially "NLP on session notes."

  3. Jennings, A. M., & Cox, D. J. (2024). Starting the conversation around the ethical use of AI in ABA. Behavior Analysis in Practice, 17, 107–122. https://pmc.ncbi.nlm.nih.gov/articles/PMC10891004/ How we cite: ethics spine — BACB 2.03/2.05, HIPAA, explainability criteria our stack satisfies by construction.

  4. Peck, S., O'Brien, C., Bourret, J., & Agostinelli, D. (2025). ChatGPT versus clinician responses to questions in ABA. JABA. https://onlinelibrary.wiley.com/doi/10.1002/jaba.70029 How we cite: BCBAs already prefer LLMs blind — raises hallucination stakes.

  5. Garg, M., Raza, S., Rayana, S., Liu, X., & Sohn, S. (2025). The rise of small language models in healthcare: A comprehensive survey. arXiv:2504.17119. https://arxiv.org/abs/2504.17119 How we cite: positions our work in SLM-clinical field; ABA is a named white-space in their taxonomy.

  6. Gao, L. et al. (2025). Generative AI for assessment and treatment of autism spectrum disorders: A scoping review. Frontiers in Psychiatry. https://pmc.ncbi.nlm.nih.gov/articles/PMC12322814/ How we cite: none of the 10 studies surveyed cover BCBA workflow / session logs / on-device — directly positions our contribution.

B. Stack precedents — small clinical on-device LMs (3)

  1. Zhang, T., et al. (2025). Menta: On-device SLM for mental health. arXiv:2512.02716. https://arxiv.org/abs/2512.02716 How we cite: direct recipe precedent (4B + LoRA r=16 α=32 + 4-bit on iPhone, beats 13B).

  2. MedGemma Team, Google DeepMind (2025). MedGemma Technical Report. arXiv:2507.05201. https://arxiv.org/abs/2507.05201 How we cite: Gemma family at 4B is clinically competent — direct stack-choice precedent.

  3. Dettmers, T., et al. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. NeurIPS. https://arxiv.org/abs/2305.14314 How we cite: foundational method for our QLoRA fine-tuning.

C. Data methodology (4)

  1. Wang, Y., et al. (2023). Self-Instruct. ACL. https://arxiv.org/abs/2212.10560 How we cite: foundational instruction-bootstrapping pipeline; source of ROUGE-L<0.7 dedup.

  2. Zhang, X., et al. (2023). AlpaCare. arXiv:2310.14558. https://arxiv.org/abs/2310.14558 How we cite: clinician-seeded stratification recipe — the key quality lever for medical instruction tuning.

  3. Gekhman, Z., et al. (2024). Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? EMNLP. https://arxiv.org/abs/2405.05904 How we cite: grounds our "teach format, not new facts" design.

  4. Patel, M., et al. (2025). How to Design, Create, and Evaluate an Instruction-Tuning Dataset in Health Care. JMIR. https://www.jmir.org/2025/1/e70481 How we cite: our validation protocol (4-dim rubric, κ with cooldown) follows this tutorial directly.

D. Evaluation methodology (7)

  1. Singhal, K., et al. (2025). Toward expert-level medical question answering with LLMs (Med-PaLM 2). Nature Medicine. https://www.nature.com/articles/s41591-024-03423-7 How we cite: rubric design (9 axes, pairwise ranking) — the clinical-NLG gold standard.

  2. Arora, R. K., et al. (2025). HealthBench. arXiv:2505.08775. https://arxiv.org/abs/2505.08775 How we cite: justifies our 3-point rubric scale over Likert-5/7.

  3. Kim, S., et al. (2024). Prometheus 2. arXiv:2405.01535. https://arxiv.org/abs/2405.01535 How we cite: the open, local LLM judge we run alongside GPT-4.1 and Claude.

  4. Zheng, L., et al. (2024). Judging LLM-as-a-Judge (MT-Bench). NeurIPS 2023. https://arxiv.org/abs/2306.05685 How we cite: sources of position / verbosity / self-enhancement bias; justifies our swap-augmentation and panel diversity.

  5. Kulkarni et al. (2025). TN-Eval: Behavioral therapy note rubrics. arXiv:2503.20648. https://arxiv.org/abs/2503.20648 How we cite: closest adjacent rubric; faithfulness warning (therapists preferred hallucinated notes).

  6. Manakul, P., et al. (2023). SelfCheckGPT. arXiv:2303.08896. https://arxiv.org/abs/2303.08896 How we cite: our zero-resource hallucination metric.

  7. Reiter, E. (2018). A Structured Review of the Validity of BLEU. Computational Linguistics, 44(3), 393–401. https://direct.mit.edu/coli/article/44/3/393/ How we cite: justifies NOT using BLEU as a primary metric (negation matters in evaluation claims).

E. Statistical grounding (3)

  1. Cohen, J. (1968). Weighted kappa. Psychological Bulletin, 70(4). How we cite: quadratic-weighted κ for our ordinal escalation-level classifier.

  2. Chicco, D., & Jurman, G. (2020). Advantages of MCC over F1 and accuracy. BMC Genomics. https://link.springer.com/article/10.1186/s12864-019-6413-7 How we cite: justifies reporting MCC alongside macro-F1 on imbalanced classification heads.

  3. Guo, C., et al. (2017). On Calibration of Modern Neural Networks. ICML. https://arxiv.org/abs/1706.04599 How we cite: source of ECE + temperature-scaling for our confidence calibration.

F. ABA foundation (5)

  1. Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson. ISBN 978-0134752556. How we cite: canonical ABA reference — chapter citations for DTT, task analysis, measurement, IOA, FBA, BIP components.

  2. Iwata, B. A., Dorsey, M. F., Slifer, K. J., Bauman, K. E., & Richman, G. S. (1982/1994). Toward a functional analysis of self-injury. JABA, 27(2), 197–209. https://doi.org/10.1901/jaba.1994.27-197 How we cite: bedrock of the four-function taxonomy we encode.

  3. Carr, E. G., & Durand, V. M. (1985). Reducing behavior problems through functional communication training. JABA, 18(2), 111–126. https://doi.org/10.1901/jaba.1985.18-111 How we cite: origin of FCT; replacement-behavior framework.

  4. Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of problem behavior: A review. JABA, 36(2), 147–185. https://doi.org/10.1901/jaba.2003.36-147 How we cite: definitive FBA review, finalization of four-function taxonomy.

  5. Lovaas, O. I. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. JCCP, 55(1), 3–9. https://doi.org/10.1037/0022-006X.55.1.3 How we cite: DTT / EIBI historical origin.

G. Dataset documentation (2)

  1. Gebru, T., et al. (2021). Datasheets for Datasets. CACM. https://arxiv.org/abs/1803.09010 How we cite: template for our dataset's datasheet appendix.

  2. Bender, E., & Friedman, B. (2018). Data Statements for NLP. TACL. https://aclanthology.org/Q18-1041/ How we cite: NLP-specific data disclosure — complements Gebru datasheet.

What was intentionally dropped (and why)

Comprehensive background is in literature-foundation.md. The following categories do not earn spots in the paper's References:

  • Target-behavior JABA papers (Kodak elopement, Piazza pica, Marcus aggression, Rapp & Vollmer stereotypy, etc.) — These ground the dataset's operational definitions and belong in the dataset card / datasheet, not the main paper. They'll appear in a supplementary taxonomy-v1.md.
  • Curriculum primary sources beyond VB-MAPP (AFLS, ABLLS-R, Essential for Living, PEAK) — cite only if we actually use them as data sources. Current pipeline uses VB-MAPP only; others stay in background reading.
  • Secondary synthetic-data papers (phi-1, Orca, LIMA, Baize, MedAlpaca, Clinical Camel, Persona Hub, Evol-Instruct, LAB, DataDreamer) — Self-Instruct + AlpaCare + Gekhman + Patel cover our method grounding. The rest are design inspiration, not required citations.
  • Secondary evaluation papers (GPTScore, JudgeLM, BARTScore, Med-HALT, MedHallu, HELM critiques, Abacha MEDIQA, G-Eval) — Prometheus 2 + MT-Bench + TN-Eval + SelfCheckGPT + Reiter + Med-PaLM 2 cover our method grounding.
  • Older measurement / IOA JABA papers (Powell 1975, Harrop & Daniels 1986, Kazdin 1977) — covered by Cooper/Heron/Heward textbook chapters.
  • Teaching-method origins beyond DTT (Hart & Risley 1975 NET, Koegel 1987 PRT, Parsons 2012 BST, Tiger 2008 FCT review, Slocum & Tiger 2011 chaining) — covered by CHH textbook chapter citations. Only cite these JABA papers individually if our paper makes a specific claim about that teaching method.
  • Neuromnia blog / Meta-AI press — not citable in an academic venue; mention in passing without reference.
  • Lanovaz & Hranchuk 2021 — different task (visual-inspection binary classification from graphs); only cite if we explicitly contrast against it. Likely drop.
  • Dataset documentation meta-papers (Bender 2023 Data Statements v2, HF dataset card docs, NeurIPS 2025 D&B CfP, Giuffrè 2023 synthetic health, Pezoulas 2025 privacy, Mozilla/AI-Alliance 2024) — cite only where directly relevant in the datasheet, not in the main paper.

Summary

Category Count
ABA × NLP 6
Stack precedents 3
Data methodology 4
Evaluation methodology 7
Statistical grounding 3
ABA foundation 5
Dataset documentation 2
Total 30

Appropriate for a 4-page ACL paper (typical range 20–40 citations) plus a NeurIPS D&B dataset paper appendix. Add more only when the text actually needs them.