mlsanityData to 7 Oct 2026

Graphical Reasoning: LLM-based Semi-Open Relation Extraction

arXiv 2405.00216 ↗First posted 30 Apr 2024cs.CL cs.AI cs.LGcited by 4

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

This paper presents a comprehensive exploration of relation extraction utilizing advanced language models, specifically Chain of Thought (CoT) and Graphical Reasoning (GRE) techniques. We demonstrate how leveraging in-context learning with GPT-3.5 can significantly enhance the extraction process, particularly through detailed example-based reasoning. Additionally, we introduce a novel graphical reasoning approach that dissects relation extraction into sequential sub-tasks, improving precision and adaptability in processing complex relational data. Our experiments, conducted on multiple datasets, including manually annotated data, show considerable improvements in performance metrics, underscoring the effectiveness of our methodologies.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 2 of its 12 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

2 of 12 references.

References · 2

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2026GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Basescited by 6
  2. 2025Can an LLM Induce a Graph? Investigating Memory Drift and Context Lengthcited by 3
  3. 2025Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasetscited by 1
  4. 2025Relation as a Prior: A Novel Paradigm for LLM-based Document-level Relation Extractioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
  2. 2026ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
  3. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
  4. 2023Revisiting Relation Extraction in the era of Large Language Models
  5. 2023Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction
  6. 2024Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
  7. 2025Grounding LLM Reasoning with Knowledge Graphs
  8. 2024Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning