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/.
By Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
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.
By Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
arXiv:2608. 06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM).
By Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
BELXTR is a new biomedical entity linking model that uses a multi‑vector (late interaction) architecture to preserve token‑level matching information, unlike traditional embedding‑based approaches that compress mentions into a single vector. By extending the XTR model with a task‑specific training objective and active query expansion, BELXTR achieves state‑of‑the‑art performance on half of ten evaluated corpora, with an average 5‑percentage‑point gain in recall@1. The model shows especially strong results on cross‑species gene disambiguation, outperforming an LLM‑powered retrieve‑and‑rerank pipeline and approaching a specialized rule‑based system.
By Samuele Garda, Ulf Leser
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
By Yi Wang, Corina Dima, Liangyu Zhong, Steffen Staab
The paper investigates modular entity disambiguation by separating candidate retrieval from entity selection. It compares sparse retrieval (BM25), Web KB search, and a dense retriever, all paired with large language model selectors. Results show that a training‑free BM25 retriever combined with an LLM selector achieves state‑of‑the‑art performance on the ZELDA benchmark, and the modular approach enables abstention when retrieval fails.
By Fina Polat, Daniel Daza, Pengyu Zhang, Klim Zaporojets, Paul Groth
arXiv:2609.24372v1 Announce Type: new
Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limi...
By Jingyu Wang, Shijie Wu, Fusheng Jin
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...
By Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti, Carlo Luschi, Daniel Justus
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.
By Binquan Ji, Jiaqi Wang, Ruiting Li, Xingchen Han, Yiyang Qi, Shichao Wang, Yifei Lu, Yuantao Han, Feiliang Ren
The paper introduces a new perspective on entity rarity in multimodal entity linking by using knowledge‑graph structural metrics instead of popularity metrics, revealing many rare entities previously overlooked. Experiments show that state‑of‑the‑art models suffer a 15.4–39.9% accuracy drop on these rare‑entity slices. The authors propose a training‑free framework that combines reasoning and retrieval with a vision‑language model, achieving a 6.9% overall accuracy gain and up to 23.3% improvement on rare entities, and release a new benchmark MERLIN‑Rare for focused evaluation.
By Parinthapat Pengpun, Simran Khanuja, Graham Neubig
arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.
By Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen
The paper presents an end‑to‑end pipeline for automatically constructing a tree‑structured knowledge graph (KG) from Vietnamese high school history textbooks and evaluating retrieval strategies that exploit the KG’s hierarchical structure. The KG construction uses a three‑phase hybrid relation extraction process, including intra‑batch deduplication, approximate cross‑batch search, and LLM extraction with a centroid filter and dual‑LLM validator, resulting in 750 nodes and 4,341 semantic edges across 41 ontology types. Retrieval evaluation compares three graph traversal strategies—Top‑Down, Horizontal, and Bottom‑Up—on a benchmark of 1,210 Vietnamese queries, finding that the Top‑Down strategy with structural information outperforms a vector baseline by 4.7 percentage points in NDCG@10.
By Ket Doan Nguyen, Minh N. H. Nguyen