Sealed job
sealed
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shards scholar.openalex · encyclopedia.wikipedia
local mesh 8 on-topic claims
queued local mesh: 8 on-topic claims (relevance gated) packed DEAI_RESEARCH_v1 JobSubmit blob (914 bytes, cid deai-research-named-shard-v1) named shards: scholar.openalex, encyclopedia.wikipedia research shards scholar.openalex,encyclopedia.wikipedia JobSealed opened; grafting claim pack searching scholar.openalex… scholar.openalex 4 hits searching encyclopedia.wikipedia… encyclopedia.wikipedia 4 hits reused 1 cids, new 2 on-topic sources, extracted 5 claims, grafted 2 links; dropped 5 off-topic hits and 0 off-topic local claims (Knowledge Graphs & Entity Linking (and their relation to LLMs); Knowledge graph entity linking via interactive reasoning and exploration with GRASP; Semantic layer; Knowledge Graph Entity Linking using Graph Embeddings; Prompt engineering) sealed review held · labels 12 · map links 2 library artifacts minted
reused 1 · new sources 2 · new claims 5 · new links 2
signature 00caee5dddfc7233d2db99ad9369e4b979eb9f3bdef1e7d368a55e3c8c488fed4ca1fa3a712606dcb74d86dd7c0474521bf7357ec3793235d7787536e387ef0f
The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. — Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering (2024)
We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). — Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering (2024)
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval to improve factual consistency and reduce hallucinations. — Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review (2025)
Retrieval-Augmented Generation (RAG) overcomes the main barrier for the adoption of LLM-based chatbots in education: hallucinations. — Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications (2025)
This artifact is information and a reusable solution. Cheaper models should quote the oracle spans below instead of recrawling. Scan GET /library and GET /api/v1/match?q= before minting a new job.