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REAP system extracts knowledge from LLMs for AKBC Shared Task 2026

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a system called REAP for the AKBC Shared Task 2026, which focuses on building knowledge bases from language models without fine-tuning. REAP utilizes structured chain-of-thought reasoning and relation-specific queries to extract knowledge, which is then formatted into JSON. Built on the Mistral-Small-24B-Instruct-2501 model, REAP achieved a macro-F1 score of 0.62 on the test set, demonstrating strong performance on specific relation types like countryLandBordersCountry and companyTradesAtStockExchange. AI

IMPACT This research demonstrates a method for extracting structured knowledge from LLMs, potentially improving the accuracy and efficiency of knowledge base creation for specific domains.

RANK_REASON The cluster describes a research paper detailing a system for knowledge base construction from LLMs, including specific performance metrics and model details. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

REAP system extracts knowledge from LLMs for AKBC Shared Task 2026

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The cluster describes a research paper detailing a system for knowledge base construction from LLMs, including specific performance metrics and model details. [lever_c_demoted from research: ic=1 a…
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Thanh-Dan Bui, Thanh-Trung Do, Tuan-Phong Nguyen ·

    REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs

    arXiv:2608.10963v1 Announce Type: new Abstract: We present the REAP system for the AKBC Shared Task 2026 on constructing knowledge bases from language models in a closed-book setting, subject to a budget of at most 32B parameters and no model fine-tuning. Our system combines stru…

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