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    <abstract>Large language models (LLMs) can fluently verbalize statistical evidence, yet statistical reports can still drift numerical values, invert effect directions, or restate thresholded contrasts as categorical effects. We frame these failures as a control problem: the evidence-bearing content of a scientific report should be fixed by structured statistical results rather than sampled during prose generation. We therefore use cross-run reproducibility to stress-test whether report-visible numbers and claims are bound before prose generation. Existing controls operate at the text or slot level; a deterministic hybrid template reproduces only 61.1% of report-visible numerical content across seeds because the LLM still selects which findings and numbers the template renders. We propose claim-locked reporting, a provenance-before-prose protocol that fixes the evidence source, numbers, direction, and allowed language strength of each reportable claim before the LLM writes connective wording. Across fMRI functional-connectivity reporting and randomized controlled trial reporting on Evidence Inference 2.0, claim-locked reporting improves reproducibility over the hybrid template by 37.4 and 20.5 points, respectively. Blinded human audits support the observed direction-preservation and governance trends. In an fMRI cost analysis with DeepSeek, claim-locked reporting also yields the lowest observed token use and median generation latency. Code is available at https://github.com/XiuFan719/Claim-Locked-Reporting.</abstract>
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%0 Conference Proceedings
%T Provenance Before Prose: Claim-Locked Reporting for Statistical Text Generation
%A Fan, Xiao
%A Li, Jingyuan
%A Guo, Hongbin
%A Han, Yubo
%A Zhang, Yi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fan-etal-2026-provenance
%X Large language models (LLMs) can fluently verbalize statistical evidence, yet statistical reports can still drift numerical values, invert effect directions, or restate thresholded contrasts as categorical effects. We frame these failures as a control problem: the evidence-bearing content of a scientific report should be fixed by structured statistical results rather than sampled during prose generation. We therefore use cross-run reproducibility to stress-test whether report-visible numbers and claims are bound before prose generation. Existing controls operate at the text or slot level; a deterministic hybrid template reproduces only 61.1% of report-visible numerical content across seeds because the LLM still selects which findings and numbers the template renders. We propose claim-locked reporting, a provenance-before-prose protocol that fixes the evidence source, numbers, direction, and allowed language strength of each reportable claim before the LLM writes connective wording. Across fMRI functional-connectivity reporting and randomized controlled trial reporting on Evidence Inference 2.0, claim-locked reporting improves reproducibility over the hybrid template by 37.4 and 20.5 points, respectively. Blinded human audits support the observed direction-preservation and governance trends. In an fMRI cost analysis with DeepSeek, claim-locked reporting also yields the lowest observed token use and median generation latency. Code is available at https://github.com/XiuFan719/Claim-Locked-Reporting.
%U https://aclanthology.org/2026.emnlp-main.357/
%P 8180-8194

Markdown (Informal)

[Provenance Before Prose: Claim-Locked Reporting for Statistical Text Generation](https://aclanthology.org/2026.emnlp-main.357/) (Fan et al., EMNLP 2026)

• Provenance Before Prose: Claim-Locked Reporting for Statistical Text Generation (Fan et al., EMNLP 2026)

ACL

• Xiao Fan, Jingyuan Li, Hongbin Guo, Yubo Han, and Yi Zhang. 2026. Provenance Before Prose: Claim-Locked Reporting for Statistical Text Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8180–8194, Budapest, Hungary. Association for Computational Linguistics.