AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed framework, KGP-QG (Knowledge Graph Based Prompting for Question Generation), which has outperformed the existing methodology on multi-hop question generation has outperformed the existing methodology.
GCA-KBQA is proposed, a step-wise fine-tuned LLM-based framework that employs hop-wise generation, knowledge-assisted calibration, and path-level assembly to construct complete LFs for KBQA, demonstrating its superior performance compared to state-of-the-art baselines.
PYTHIA is presented, a training-free, plug-and-play solution for KGQA over any RDF KG which consists of an LLM agent guided by a relation-centric conceptual model of the KGQA task which is acted upon through a suite of tools for entity linking, graph exploration and query execution.
GraphQAG effectively supports users in identifying knowledge coverage gaps, examining generated QA pairs, and refining the QA pair set through graph-based interactions, demonstrating the usefulness of combining knowledge graphs, LLM-based generation, and visual analytics for producing more comprehensive and trustworthy QA pairs from long documents.
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
GraphSynthQA, a knowledge-graph)—guided synthesis framework in an open-web setting, which iteratively retrieves and verifies evidence from the internet to expand a KG, then synthesizes complex, answer-verifiable queries grounded in multi-evidence dependencies.
KGCaRe is proposed, a hybrid approach that combines neural retrieval with symbolic reasoning over LLM-generated KGs that consistently outperforms existing baselines, including Vanilla LLM, Code Prompt, Text Prompt, Think-on-Graph, Vanilla RAG, and HybridContextQA.
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