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Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) results in complex systems with numerous hyperparameters that directly affect performance.
“The value of semantic parse labeling for knowledge base question answering”
Wen-tau Yih et al · 2016
Earlier work this paper cites.
“Attention is all you need”
Ashish Vaswani et al · 2017
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“The web as a knowledge-base for answering complex questions”
Alon Talmor and Jonathan Berant · 2018
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“HotpotQA: A dataset for diverse, explainable multi-hop question answering”
Zhilin Yang et al · 2018
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“Variational reasoning for question answering with knowledge graph”
Yuyu Zhang et al · 2018
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“Neural legal judgment prediction in English”
Ilias Chalkidis, Ion Androutsopoulos and Nikolaos Aletras · 2019
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“Language models as knowledge bases?”
Fabio Petroni et al · 2019
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“From Louvain to Leiden: guaranteeing well-connected communities”
Vincent Traag, Ludo Waltman and Nees Van · 2019
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“Retrieval augmented language model pre-training”
Kelvin Guu et al · 2020
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“Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps”
Xanh Ho, Anh-Khoa Nguyen, Saku Sugawara and Akiko Aizawa · 2020
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“Retrieval-augmented generation for knowledge-intensive nlp tasks”
Patrick Lewis et al · 2020
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“How much knowledge can you pack into the parameters of a language model?”
Adam Roberts, Colin Raffel and Noam Shazeer · 2020
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“Neural, symbolic and neural-symbolic reasoning on knowledge graphs”
Jing Zhang et al · 2021
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“MuSiQue: Multihop Questions via Single-hop Question Composition”
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot and Ashish Sabharwal · 2022
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“Subgraph retrieval enhanced model for multi-hop knowledge base question answering”
Jing Zhang et al · 2022
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“The impact of large language models on scientific discovery: a preliminary study using gpt-4”
Microsoft AI4Science and Microsoft Quantum · 2023
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“Self-rag: Learning to retrieve, generate, and critique through self-reflection”
Akari Asai et al · 2023
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“Complex logical reasoning over knowledge graphs using large language models”
Nurendra Choudhary and Chandan Reddy · 2023
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“Retrieval-augmented generation for large language models: A survey”
Yunfan Gao et al · 2023
“Retrieval-augmented generation with graphs (graphrag)”
Haoyu Han et al · 2024
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“G-retriever: Retrieval-augmented generation for textual graph understanding and question answering”
Xiaoxin He et al · 2024
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“Introducing a new hyper-parameter for RAG: Context Window Utilization”
Kush Juvekar and Anupam Purwar · 2024
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“The dawn after the dark: An empirical study on factuality hallucination in large language models”
Junyi Li et al · 2024
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“Unifying large language models and knowledge graphs: A roadmap”
Shirui Pan et al · 2024
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“Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems”
Matthew Barker et al · 2025
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“Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning”
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