SOURCE-LINKED INTELLIGENCE
DiffIE: Diffusion-based Open Information Extraction
A single sentence often expresses multiple valid relational triplets, which makes Open Information Extraction (OpenIE) fundamentally a multi-output task. Existing neural systems handle this by autoregressive generation, which is flexible but slow and prone to redundancy, or by fixed-slot prediction, which is efficient but couples the extraction budget to training. We introduce DIFFIE which instead treats the stochasticity of conditional discrete diffusion as the extraction mechanism itself: independent reverse-diffusion trajectories over per-token role tags produce a pool of candidate triplets
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T09:01:42.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.