Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Large Language Models are enhanced by iterative reasoning through frameworks like KG-IRAG [1] and techniques such as chain-of-thought prompting [2]. Furthermore, iterative reasoning serves as a method to increase test-time computation for these models [3] and facilitates complex multi-hop question answering when combined with knowledge graphs [4].
Facts (5)
Sources
A Survey on the Theory and Mechanism of Large Language Models arxiv.org 2 facts
claimPerformance gains in large language models are achieved not only by scaling data and model size during training, but also by increasing test-time computation, such as allowing the model to perform recurrent or iterative reasoning.
claimChain-of-thought (CoT) reasoning has significantly increased the expressive power of large language models, leading researchers to investigate how to implicitly incorporate iterative reasoning into a model's inductive bias.
KG-IRAG with Iterative Knowledge Retrieval - arXiv arxiv.org 1 fact
claimKnowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG) is a framework that integrates Knowledge Graphs with iterative reasoning to improve Large Language Models' ability to handle queries involving temporal and logical dependencies.
Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org 1 fact
procedureThere are four primary methods for integrating Knowledge Graphs with Large Language Models: (1) learning graph representations, (2) using Graph Neural Network (GNN) retrievers to extract entities as text input, (3) generating code like SPARQL queries to retrieve information, and (4) using step-by-step interaction methods for iterative reasoning.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org 1 fact
claimFusing knowledge from LLMs and Knowledge Graphs augments question decomposition in multi-hop Question Answering, facilitating iterative reasoning to generate accurate final answers.