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ReLTEx is a framework for reliable LLM-based taxonomy expansion that combines LLM-driven candidate generation with structure-aware validation and recursive expansion control to reduce hallucinations and improve consistency. Experiments on benchmark taxonomies show it produces more reliable and semantically coherent expansions.
This paper introduces hierarchical domain generalization, formalizing extrapolation from finite observed regions to an entire instance space. It shows that no matter how simple the hypothesis class, certain domain partitions make generalization impossible, arguing that modern generalization theory must incorporate domain structure.
This paper proposes LAD-inspired pre-pretraining using a formal language called MP-Struct that encodes natural-language-like structures. It shows that this approach improves token efficiency and imparts human-like resistance to structurally implausible languages, challenging prior hypotheses about effective pre-pretraining languages.