iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations
This work addresses a bottleneck in causal discovery from text by providing scalable, high-quality annotated data for benchmarking algorithms, though it is incremental as it builds on existing LLM-dependent methods.
The paper tackles the problem of generating text with accurate causal graph annotations, which is needed for causal discovery from text but lacks ground truth data due to high annotation costs. The proposed iTAG method achieves both high annotation accuracy and naturalness in generated text, with results showing high statistical correlation with real-world data when used to test text-based causal discovery algorithms.
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. Therefore, we propose iTAG, which performs real-world concept assignment to nodes before converting causal graphs into text in existing LLM-dependent methods. iTAG frames this process as an inverse problem with the causal graph as the target, iteratively examining and refining concept selection through Chain-of-Thought (CoT) reasoning so that the induced relations between concepts are as consistent as possible with the target causal relationships described by the causal graph. iTAG demonstrates both extremely high annotation accuracy and naturalness across extensive tests, and the results of testing text-based causal discovery algorithms with the generated data show high statistical correlation with real-world data. This suggests that iTAG-generated data can serve as a practical surrogate for scalable benchmarking of text-based causal discovery algorithms.