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            <namePart type="given">Yimu</namePart>
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    <abstract>Slide narration generation can introduce unsupported visual or decorative claims. We describe a pipeline that indexes slide content with deterministic identifiers, generates an internally cited template, and applies heuristic checks before and after an optional language-model rewrite. A narration-stage study covers 30 publicly sourced presentations (279 slides). The same decks and procedure were used, but stochastic vision evidence was regenerated by condition. In one run per condition, the complete configuration produced 5/279 proxy flags (1.79%) versus 22/279 (7.89%) for A1, a 77.3% relative difference (slide-paired exact p \ensuremath\approx 2.2 × 10\ensuremath⁻\ensuremath⁴; deck-blocked p = 0.0156); B produced 20/279 (7.17%). Saved labels mark 225/279 condition-C outputs as template fallbacks. Post-rewrite verdicts were not saved, and six outputs retained model narration when no nonempty verified template was available. The proxy has F1 = 0.60 against judgments based on extracted context rather than rendered slides. These are observed system-level proxy rates, not component attribution or evidence that hallucination is eliminated.</abstract>
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%0 Conference Proceedings
%T Evidence-Indexed Slide Narration With Heuristic Checks and Template Fallback
%A Gupta, Sachin
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F gupta-2026-evidence
%X Slide narration generation can introduce unsupported visual or decorative claims. We describe a pipeline that indexes slide content with deterministic identifiers, generates an internally cited template, and applies heuristic checks before and after an optional language-model rewrite. A narration-stage study covers 30 publicly sourced presentations (279 slides). The same decks and procedure were used, but stochastic vision evidence was regenerated by condition. In one run per condition, the complete configuration produced 5/279 proxy flags (1.79%) versus 22/279 (7.89%) for A1, a 77.3% relative difference (slide-paired exact p \ensuremath\approx 2.2 × 10\ensuremath⁻\ensuremath⁴; deck-blocked p = 0.0156); B produced 20/279 (7.17%). Saved labels mark 225/279 condition-C outputs as template fallbacks. Post-rewrite verdicts were not saved, and six outputs retained model narration when no nonempty verified template was available. The proxy has F1 = 0.60 against judgments based on extracted context rather than rendered slides. These are observed system-level proxy rates, not component attribution or evidence that hallucination is eliminated.
%U https://aclanthology.org/2026.groundlm-1.78/
%P 1116-1123

Markdown (Informal)

[Evidence-Indexed Slide Narration With Heuristic Checks and Template Fallback](https://aclanthology.org/2026.groundlm-1.78/) (Gupta, GroundLM 2026)

• Evidence-Indexed Slide Narration With Heuristic Checks and Template Fallback (Gupta, GroundLM 2026)

ACL

• Sachin Gupta. 2026. Evidence-Indexed Slide Narration With Heuristic Checks and Template Fallback. In Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026), pages 1116–1123, Budapest, Hungary. Association for Computational Linguistics.