Q1. 请解释LAMA如何使用知识图谱来探测LLMs中的知识。 LAMA(Language Model Analysis with Knowledge Attention)是一种方法,用于探测大型语言模型(LLMs)中存储的知识,特别是与知识图谱(KGs)相关的知识。LAMA的核心思想是通过将知识图谱中的事实与特定的提示模板结合,使LLMs预测这些提示中被遮蔽的信息,从而评估模型是否...
从使用的角度看，LLM就像是一个知识库，有点像之前knowledge graph。相比knowledge graph，LLM通用性更强...
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[11] Enhancing the Accuracy of RAG Applications With Knowledge Graphs, Blog:https://neo4j.com/developer-blog/enhance-rag-knowledge-graph/ [12] 利用知识图谱提升RAG应用的准确性, [11]的中文版:https://zhuanlan.zhihu.com/p/692595027 [13] LLMGraphTransformer, Langchain:https://python.langchain.com...
● Knowledge graph-enhanced molecular contrastive learning with functional prompt, Nature Machine Intelligence, 2023. ● Direct prediction of gas adsorption via spatial atom interaction learning, Nature Communications, 2023. ● Learning Invariant Molecular Representation in Latent Discrete Space, NeurIPS, 2023...
Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models NAACL Short 2024 GitHub CodeKGC: Code Language Model for Generative Knowledge Graph Construction ACM TALLIP 2024-03 GitHub On-the-fly Definition Augmentation of LLMs for Biomedical NER NAACL 2024-03 GitHub Leveraging Chat...
KD of LLMs: This survey delves into knowledge distillation (KD) techniques in Large Language Models (LLMs), highlighting KD's crucial role in transferring advanced capabilities from proprietary LLMs like GPT-4 to open-source counterparts such as LLaMA and Mistral. We also explore how KD enables...
De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution, Nature Communications, 2022. OntoProtein: Protein Pretraining With Gene Ontology Embedding, ICLR, 2022. Molecular Contrastive Learning with Chemical Element Kno...
Recently, integrated graph-language architectures that synergize the complementary strengths of GNN encoders and LLM decoders have gained prominence. As summarized in a survey paper (Li et al. 2023), these integrated approaches can be categorized based on the role played by LLMs: ...
This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis ...