REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs
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Computer Science > Computation and Language
Title:REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs
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 structured chain-of-thought reasoning, relation-specific query strategies, and a reasoning-based empty-set gate to elicit parametric knowledge, followed by direct extraction into valid JSON arrays. On the test set, the system, built on the Mistral-Small-24B-Instruct-2501 model, achieves a macro-F1 score of 0.62, with particularly strong results on countryLandBordersCountry (F1 = 0.95), companyTradesAtStockExchange (F1 = 0.73), and hasArea (F1 = 0.77). Our code is publicly available at this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.10963 [cs.CL] |
| (or arXiv:2608.10963v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10963
arXiv-issued DOI via DataCite (pending registration)
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