목차
KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and LLM s, which is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG can effectively overcome the ambiguity of traditional RAG vector similarity calculation and the noise problem of GraphRAG introduced by OpenIE. KAG supports logical reasoning and multi-hop fact Q&A, etc., and is significantly better than the current SOTA method.
github.com/OpenSPG/KAG
목차 관련 글 Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks “Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models b…” Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks “We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combin…” HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems “Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the halluci…” Hypothetical Document Embeddings “This vector identifies a neighborhood in the corpus embedding space, from which similar real documents are retr…” The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity “While our puzzle environments enable controlled experimentation with fine-grained control over problem complexi…” iText2KG: Incremental knowledge graphs construction using large language models “Automatically building knowledge graphs (KGs) is crucial for structuring data and making it accessible, allowin…” LazyGraphRAG “… we introduce a radically different approach to graph-enabled RAG that requires no prior summarization of the …” A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions “Additionally, we delve into the current limitations faced by retrieval-augmented LLMs in combating hallucinatio…” Contextual retrieval “This method can reduce the number of failed retrievals by 49% and, when combined with reranking, by 67%.” AI와 지식노동 주제어가 겹침 On the Theoretical Limitations of Embedding-Based Retrieval 관련 절: Empirical Connection: Real-World Datasets Archon (software) “For you, it's a sleek interface to manage knowledge, context, and tasks for your projects.”