When Looks Do Not Lie: Discourse Structure Guided In-Context Learning for Faithful Diagram Generation

Abstract:GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination. We introduce a novel method for ICL diagram generation based on Rhetorical Structure Theory, which improves diagram faithfulness to its source text context. We find that ICL performance depends on task distribution and models' reasoning ability, with higher reasoning allowing better quality and performance for an out-of-distribution task. We perform an expert evaluation of 150 generated diagrams and analyze our findings using Bayesian GLMMs. Additionally, we use our evaluation rubric and samples from the data set for automated diagram evaluation, achieving statistically significant agreement with human evaluation.

Computation and Language (cs.CL)

arXiv:2601.20476 [cs.CL]

(or arXiv:2601.20476v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.20476 Focus to learn more arXiv-issued DOI via DataCite

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