Cognitive Mirroring for DocRE: A Self-Supervised Iterative Reflection Framework with Triplet-Centric Explicit and Implicit Feedback

AbstractLarge language models (LLMs) have advanced document-level relation extraction (DocRE), but DocRE is more complex than sentence-level relation extraction (SentRE), facing challenges like diverse relation types, coreference resolution and long-distance dependencies. Traditional pipeline methods, which detect relations before generating triplets, often propagate errors and harm performance. Meanwhile, fine-tuning methods require extensive human-annotated data, and in-context learning (ICL) underperforms compared to supervised approaches. We propose an iterative reflection framework for DocRE, inspired by human non-linear reading cognition. The framework leverages explicit and implicit relations between triplets to provide feedback for LLMs refinement. Explicit feedback uses logical rules-based reasoning, while implicit feedback reconstructs triplets into documents for comparison. This dual-process iteration mimics human semantic cognition, enabling dynamic optimization through self-generated supervision. For the first time, this achieves zero-shot performance comparable to fully supervised models. Experiments show our method surpasses existing LLM-based approaches and matches state-of-the-art BERT-based methods.