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related 11.00 — strongly supporting 11 facts
Large Language Models are frequently utilized to perform Named Entity Recognition (NER) tasks, as evidenced by specific methodologies like 'GPT-NER' [1] and their integration into knowledge graph construction pipelines {fact:1, fact:4}. Furthermore, research compares the performance of Large Language Models against fine-tuned models for NER [2] and explores how prompting techniques can guide these models in extracting entity types {fact:5, fact:6}.
Facts (11)
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A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 3 facts
procedureThe process of integrating KGs with LLMs begins with data preparation, which involves extracting entities and relationships from KGs using techniques like Named Entity Recognition (NER) and relation extraction.
procedureThe LLM-augmented KG process is structured into two principal stages: (1) synthesizing KGs by applying LLMs to perform coreference resolution, named entity recognition, and relationship extraction to relate entities from input documents; (2) performing tasks on the constructed KG using LLMs, including KG completion to fill gaps, KG question answering to query responses, and KG text generation to develop descriptions of nodes.
claimLarge Language Models (LLMs) perform sentiment classification, topic categorization, and named entity recognition (NER) to identify names, dates, and locations.
Combining large language models with enterprise knowledge graphs frontiersin.org 3 facts
claimPrompting with large Large Language Models (like GPTs) can underperform in Named Entity Recognition compared to fine-tuned smaller Pre-trained Language Models (like BERT derivations), especially when more training data is available (Gutierrez et al., 2022; Keloth et al., 2024; Pecher et al., 2024; Törnberg, 2024).
procedurePrompting for Named Entity Recognition involves using entity definitions, questions, sentences, and output examples to guide Large Language Models in understanding entity types and extracting answers (Ashok and Lipton, 2023; Kholodna et al., 2024).
referenceRecent literature identifies two primary approaches to named entity recognition and relation extraction: creating large training sets with hand-curated or extensive automatic annotations to fine-tune large language models, or using precise natural language instructions to replace domain knowledge with prompt engineering.
Integrating Knowledge Graphs into RAG-Based LLMs to Improve ... thesis.unipd.it 2 facts
procedureThe proposed method for integrating knowledge graphs with LLMs utilizes Named Entity Recognition (NER) and Named Entity Linking (NEL) combined with SPARQL queries directed at the DBpedia knowledge graph.
procedureThe proposed method in the thesis integrates knowledge graphs with Large Language Models by combining Named Entity Recognition (NER) and Named Entity Linking (NEL) with SPARQL queries to the DBpedia knowledge graph.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
referenceTOPT (Zhang et al., 2024a) is a task-oriented pre-training model that utilizes Large Language Models to generate task-specific knowledge corpora to enhance domain adaptability and Named Entity Recognition sensitivity.
referenceWang et al. (2023) developed 'GPT-NER', a method for named entity recognition using large language models.
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org 1 fact
claimThe integration of Large Language Models and Knowledge Graphs improves performance in Natural Language Processing (NLP) tasks, specifically named entity recognition and relation classification.