Bulletin of the Technical Committee on Data Engineering最新文献

筛选

英文中文

Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation. 面向结构检索的知识图谱与大型语言模型协同学习。

Bulletin of the Technical Committee on Data Engineering Pub Date : 2024-12-01

Carl Yang, Ran Xu, Linhao Luo, Shirui Pan

{"title":"Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation.","authors":"Carl Yang, Ran Xu, Linhao Luo, Shirui Pan","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recent years have witnessed major technical breakthroughs in AI- facilitated by tremendous data and high-performance computers, large language models (LLMs) have brought disruptive progress to information technology from accessing data to performing analysis. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning. Despite recent works discussing the hallucination problem of LLMs, systematic studies on empowering LLMs with the ability to plan, reason, and ground with explicit knowledge are still lacking. On the other hand, real-world data are enormous and complex, coming from different sources and bearing various modalities. Data professionals have spent tremendous efforts collecting and curating countless datasets with different schemas and standards. Transforming the separate datasets into unified knowledge graphs (KGs) can facilitate their integrative analysis and utilization, but these processes would often require strong domain expertise and significant human labor. In this paper, we discuss recent progress and promise in the co-learning of KGs and LLMs, through LLM-aided KG construction, KG-guided LLM enhancement, and knowledge-aware multi-agent federation, particularly emphasizing a structure-oriented retrieval augmented generation (SRAG) paradigm, towards fully utilizing the value of complex data, unleashing the power of generative models, and expediting next-generation trustworthy AI.</p>","PeriodicalId":92006,"journal":{"name":"Bulletin of the Technical Committee on Data Engineering","volume":"48 4","pages":"9-46"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12998269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147488817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management. 优化开放式众包:众包数据管理的下一个前沿。

Bulletin of the Technical Committee on Data Engineering Pub Date : 2016-12-01

Aditya Parameswaran, Akash Das Sarma, Vipul Venkataraman

引用次数: 0