Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering

• URL: http://arxiv.org/abs/2404.10384v1
• Date: Tue, 16 Apr 2024 08:28:16 GMT
• Title: Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
• Authors: Yuqi Wang, Boran Jiang, Yi Luo, Dawei He, Peng Cheng, Liangcai Gao,
• Abstract summary: Large language models (LLMs) perform surprisingly well and outperform human experts on many tasks. This paper integrates and optimized a pipeline for selecting reasoning paths from KG based on LLM. We also propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank.
• Score: 18.94220625114711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs), such as GPT3.5, GPT4 and LLAMA2 perform surprisingly well and outperform human experts on many tasks. However, in many domain-specific evaluations, these LLMs often suffer from hallucination problems due to insufficient training of relevant corpus. Furthermore, fine-tuning large models may face problems such as the LLMs are not open source or the construction of high-quality domain instruction is difficult. Therefore, structured knowledge databases such as knowledge graph can better provide domain back- ground knowledge for LLMs and make full use of the reasoning and analysis capabilities of LLMs. In some previous works, LLM was called multiple times to determine whether the current triplet was suitable for inclusion in the subgraph when retrieving subgraphs through a question. Especially for the question that require a multi-hop reasoning path, frequent calls to LLM will consume a lot of computing power. Moreover, when choosing the reasoning path, LLM will be called once for each step, and if one of the steps is selected incorrectly, it will lead to the accumulation of errors in the following steps. In this paper, we integrated and optimized a pipeline for selecting reasoning paths from KG based on LLM, which can reduce the dependency on LLM. In addition, we propose a simple and effective subgraph retrieval method based on chain of thought (CoT) and page rank which can returns the paths most likely to contain the answer. We conduct experiments on three datasets: GenMedGPT-5k [14], WebQuestions [2], and CMCQA [21]. Finally, RoK can demonstrate that using fewer LLM calls can achieve the same results as previous SOTAs models.

Related papers

• Exploring Language Model Generalization in Low-Resource Extractive QA [57.14068405860034]
  We investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift. We devise a series of experiments to empirically explain the performance gap.
  arXiv  Detail & Related papers  (2024-09-27T05:06:43Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• LLM-Enhanced Data Management [17.382233123729755]
  Large language models (LLMs) have shown high generalizability and human-competitive abilities in understanding context. LLMs have several limitations: hallucination, high cost, and low accuracy for complicated tasks. We design LLMDB, an LLM-enhanced data management paradigm which has generalizability and high inference ability while avoiding hallucination.
  arXiv  Detail & Related papers  (2024-02-04T23:42:02Z)
• Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Models [10.04323204974924]
  Multi-Hop Question Answering (MHQA) stands as a widely discussed category. Existing methods employ Large Language Models (LLMs) to generate reasoning paths and plans. We propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA)
  arXiv  Detail & Related papers  (2023-09-22T10:15:13Z)
• Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs [19.0797968186656]
  Large language models (LLMs) are versatile and can solve different tasks due to their emergent ability and generalizability. In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases.
  arXiv  Detail & Related papers  (2023-09-06T15:55:01Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [29.447300472617826]
  Think-on-Graph (ToG) is a new approach for external knowledge graphs (KG) in large language models (LLMs) ToG iteratively executes beam search on KG, discovers the most promising reasoning paths, and returns the most likely reasoning results. ToG achieves overall SOTA in 6 out of 9 datasets where most previous SOTAs rely on additional training.
  arXiv  Detail & Related papers  (2023-07-15T03:31:38Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• LLMMaps -- A Visual Metaphor for Stratified Evaluation of Large Language Models [13.659853119356507]
  Large Language Models (LLMs) have revolutionized natural language processing and demonstrated impressive capabilities in various tasks. They are prone to hallucinations, where the model exposes incorrect or false information in its responses. We propose LLMMaps as a novel visualization technique that enables users to evaluate LLMs' performance with respect to Q&A datasets.
  arXiv  Detail & Related papers  (2023-04-02T05:47:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.