AI RESEARCH
iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations
arXiv CS.CL
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ArXi:2604.06902v1 Announce Type: new A fundamental obstacle to causal discovery from text is the lack of causally annotated text data for use as ground truth, due to high annotation costs. This motivates an important task of generating text with causal graph annotations. Early template-based generation methods sacrifice text naturalness in exchange for high causal graph annotation accuracy. Recent Large Language Model (LLM)-dependent methods directly generate natural text from target graphs through LLMs, but do not guarantee causal graph annotation accuracy.