Cognitive Mirroring for D oc RE : A Self-Supervised Iterative Reflection Framework with Triplet-Centric Explicit and Implicit Feedback (2025)

Abstract: Large 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.

## introduction docre aims to identify entity pairs and their semantic relations within long contexts, playing a vital role in various downstream nlp applications. llms have made significant progress in classical information extraction tasks @xcite . recent studies leverage their strong instructionfollowing abilities and rich intrinsic knowledge to enhance docre performance @xcite @xcite . however, docre is more challenging than sen-tre due to the diversity of relation types, coreference resolution and long-distance dependencies within the document. consequently, many sen-tre methods @xcite cannot be directly applied to docre. to address these challenges, existing methods typically adopt a linear pipeline framework @xcite , which sequentially detects relations and generates triplets. however, this often leads to error propagation, degrading downstream performance. based on the linear framework, these methods primarily rely on two paradigms: supervised methods requiring costly human annotations, and icl approaches that underperform their supervised counterparts. existing linear frameworks do not align with the human cognitive process, which is iterative and conflict-driven rather than linear. the docre task inherently mirrors this process: when extracting relations from documents, humans naturally reread ambiguous parts and resolve conflicts (aligned with the construction-integration model @xcite and cognitive dissonance theory @xcite ), and distinguish at both explicit and implicit levels (as per dual process theory @xcite ). these theories suggest that text comprehension involves dynamic re-evaluation of earlier content when conflicts or missing information arise, not only emphasizing the iterative nature of human reading cognition, but also highlighting that the main driver of this iteration is the resolution of conflicts between knowledge. however, existing linear docre models violate these theories by processing documents unidirectionally without iterative verification, leading to suboptimal performance. this disconnect becomes evident when considering the demonstrated success of the self-correction mechanism in other nlp domains @xcite , whose potential for docre remains strikingly underexplored. the absence of such reflective capabilities in existing approaches not only violates ## related work human cognitive science cognitive theories provide key insights into dual-process iteration. construction-integration model @xcite highlights reading as a dynamic process, where readers iteratively revise their understanding in response to conflicts or missing information. this aligns with the cognitive dissonance theory @xcite , which emphasizes the importance of resolving contradictions through reflective correction. dual-process theory @xcite suggests that human cognition involves both fast, intuitive processing (system 1) and slower, controlled reasoning (system 2). these theories inform our approach, integrating non-linear iteration and dualrelation analysis to enhance docre. ## methodology as illustrated in figure 1 , the proposed framework generates feedback for iterative modification of llms by analyzing explicit and implicit relations between triplets. specifically, the framework consists of three phases: 1) initial extraction, 2) tripletcentric implicit feedback through reconstruction, and 3) explicit feedback based on logical rules. ## conclusion inspired by human non-linear reading cognition, we propose an iterative reflection framework for icl-based docre that validates both explicit and implicit relations between triplets. the framework initiates with preliminary extraction and then establishes an iterative extraction-verification-feedbackcorrection procedure. explicit relational feedback is based on logical rules-based reasoning, while feedback on implicit relations is generated by reconstructing the extracted triplets into a document and comparing it with the original. this dual-process mechanism mimics human cognition, enabling self-supervised optimization without reliance on annotated data. extensive experiments demonstrate that our framework not only narrows the performance gap with sota bert-based methods, but also performs better in complex docre task. additionally, it helps mitigate error propagation by continuously identifying and correcting errors. furthermore, our method explores the application of the self-correction mechanism in docre, offering a more efficient and convenient solution. it provides new insights and directions for the application of llms in docre and offers a valuable reference for future exploration.