The paper investigates how large language models (LLMs) handle multi‑valued relations when used as knowledge bases. It identifies a "canonical order problem," where LLMs internally organize such relations according to a fixed ordering (e.g., alphabetical or chronological). The authors analyze the generation process in three phases—retrieval, internal sorting, and selection—and show that deviating from the internal canonical order reduces the reliability of LLMs in producing complete sets for multi‑valued relations.
arXiv:2608.22762v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) is a key task for evaluating KG-augmented Large Language Models (LLMs), and complex KGQA that requires multi-...
arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
The paper introduces GPTKB 2.0, a method for building disambiguated knowledge bases directly from large language models. It addresses the lack of native entity representation in LLMs by performing on‑the‑fly disambiguation of entities, relations, and classes, achieving a million‑scale KB with over 1 million disambiguated entities and 38.4 million triples. The authors analyze trade‑offs among accuracy, scale, and cost, and release the system at https://gptkb.org/.
arXiv:2608.03729v4 Announce Type: replace-cross Abstract: Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large lang...
arXiv:2410.17355v4 Announce Type: replace Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity t...
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm.
arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
arXiv:2608. 03729v1 Announce Type: cross Abstract: Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large language models (LLMs), treating the model itself as the knowledge source.
The paper introduces CoSC, a method for knowledge graph completion that merges discrete structural coding with information from similar entities. An LLM first produces a candidate ranking based on structural codes, then refines this ranking using data from entities with comparable structures. Experiments on FB15k-237 demonstrate that CoSC achieves higher MRR and Hits@10 than existing baselines while staying competitive on Hits@1.
CONSISTRE is a consistency‑aware framework for document‑level relation extraction that tackles contradictions in large language model predictions. It offers two tracks: an inference‑time track that refines black‑box LLM outputs through constraint‑aware prompting, verification, and self‑reflection, and a training‑time track that distills consistency knowledge into smaller open‑source models via supervised fine‑tuning and reinforcement learning. Experiments on DocRED show both tracks outperform baselines, with the inference‑time track matching competitive F1 scores and the training‑time track narrowing the performance gap to proprietary LLMs while reducing inference cost.
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated independently for each candidate triple and may violate fundamental relational constraints such as transitivity, symmetry, and functional uniqueness, leading to contradictory and unreliable outputs.
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.