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KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases

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arxiv 2308.11761 v1 pith:FAFZAID6 submitted 2023-08-17 cs.CL cs.AI

Xintao Wang , Qianwen Yang , Yongting Qiu , Jiaqing Liang , Qianyu He , Zhouhong Gu , Yanghua Xiao , Wei Wang This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgellmsknowledgptlanguageretrievalbaseslargemodels
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Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the integration of knowledge bases (KBs) remains understudied and faces several challenges. In this paper, we introduce KnowledGPT, a comprehensive framework to bridge LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands. With extensive experiments, we show that by integrating LLMs with KBs, KnowledGPT properly answers a broader range of questions requiring world knowledge compared with vanilla LLMs, utilizing both knowledge existing in widely-known KBs and extracted into personalized KBs.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge

    cs.CV 2025-01 conditional novelty 6.0 of 10

    StreamChat uses hierarchical memory and three parallel threads to enable real-time multi-round video question answering, and StreamBench is a new benchmark for evaluating such streaming systems.

  2. Joint Enhancement of Relational Reasoning for Long-Context LLMs

    cs.CL 2025-08 conditional novelty 5.0 of 10

    JERR builds a directed graph of summarized text chunks and uses Monte Carlo Tree Search to retrieve relevant nodes, reportedly improving long-context QA over baselines.

  3. Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery

    cs.AI 2025-06 conditional novelty 5.0 of 10

    A learning-to-rank model chooses informative knowledge-graph paths between entity pairs, and adding the top path to zero-shot prompts improves LLM causal classification by up to 44.4 F1 points.

  4. Hallucinate, Ground, Repeat: A Framework for Generalized Visual Relationship Detection

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Training a scene graph model on LLM-generated relationship labels, with iterative self-refinement, improves mean recall on a custom Visual Genome benchmark, including predicates absent from human annotations.

  5. Xinyu AI Search: Enhanced Relevance and Comprehensive Results with Rich Answer Presentations

    cs.IR 2025-05 conditional novelty 5.0 of 10

    Xinyu, an integrated generative AI search engine with query decomposition, multi-source retrieval, and rich answer presentation, outperforms eight existing technologies in human evaluations.

  6. DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation

    cs.CL 2025-06 reject novelty 4.0 of 10

    A small model prompted with evidence and knowledge graphs generated by GPT-4o scores much higher on QA benchmarks, but the result is not true distillation and may be contaminated by teacher answer leakage.

  7. Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression

    cs.LG 2025-05 conditional novelty 4.0 of 10

    ACBench tests compressed LLMs on agentic tasks and finds 4-bit quantization keeps tool use and workflow generation strong while hurting real-world application performance.

  8. Generating a Low-code Complete Workflow via Task Decomposition and RAG

    cs.SE 2024-11 conditional novelty 4.0 of 10

    A case study and pattern catalogue arguing that Task Decomposition and RAG improve modularity, safety, testability, and time-to-market in a real enterprise workflow generator, with no quantitative evaluation.

  9. A Survey on Retrieval And Structuring Augmented Generation with Large Language Models

    cs.CL 2025-09 conditional novelty 2.0 of 10

    The paper presents a comprehensive survey and taxonomy of RAS methods, covering retrieval, text structuring, and LLM integration.

  10. Foundations of GenIR

    cs.IR 2025-01 unverdicted novelty 1.0 of 10

    A survey chapter proposing that generative AI reshapes information access through two paradigms, information generation and information synthesis.

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