Assessing LLMs Suitability for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2405.17249v2
• Date: Thu, 18 Jul 2024 09:48:02 GMT
• Title: Assessing LLMs Suitability for Knowledge Graph Completion
• Authors: Vasile Ionut Remus Iga, Gheorghe Cosmin Silaghi,
• Abstract summary: Large Language Models (LLMs) can be used to solve tasks related to Knowledge Graphs. LLMs are known to hallucinate answers, or output results in a non-deterministic manner.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or output results in a non-deterministic manner, thus leading to wrongly reasoned responses, even if they satisfy the user's demands. To highlight opportunities and challenges in knowledge graphs-related tasks, we experiment with three distinguished LLMs, namely Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125 and GPT-4o, on Knowledge Graph Completion for static knowledge graphs, using prompts constructed following the TELeR taxonomy, in Zero- and One-Shot contexts, on a Task-Oriented Dialogue system use case. When evaluated using both strict and flexible metrics measurement manners, our results show that LLMs could be fit for such a task if prompts encapsulate sufficient information and relevant examples.

Related papers

• AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data [14.328402787379538]
  We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines.
  arXiv  Detail & Related papers  (2024-10-15T12:05:58Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [90.98855064914379]
  We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs. Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy. We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
  arXiv  Detail & Related papers  (2024-09-29T11:38:45Z)
• Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Models [33.662269036173456]
  Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. Knowledge Graph Question Answering (KGQA) serves as a critical touchstone for the integration. We propose an interactive KGQA framework that leverages the interactive learning capabilities of LLMs to perform reasoning and Debating over Graphs (DoG)
  arXiv  Detail & Related papers  (2024-09-05T01:11:58Z)
• Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path [53.71787069694794]
  We focus on the graph reasoning ability of Large Language Models (LLMs) We revisit the ability of LLMs on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Our findings suggest that LLMs can fail to understand graph structures through text descriptions and exhibit varying performance for all these fundamental tasks.
  arXiv  Detail & Related papers  (2024-08-18T16:26:39Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• 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)
• Can we Soft Prompt LLMs for Graph Learning Tasks? [22.286189757942054]
  GraphPrompter is a framework designed to align graph information with Large Language Models (LLMs) via soft prompts. The framework unveils the substantial capabilities of LLMs as predictors in graph-related tasks.
  arXiv  Detail & Related papers  (2024-02-15T23:09:42Z)
• Exploring Large Language Models for Knowledge Graph Completion [17.139056629060626]
  We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction.
  arXiv  Detail & Related papers  (2023-08-26T16:51:17Z)
• Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models [53.09723678623779]
  We propose TAGREAL to automatically generate quality query prompts and retrieve support information from large text corpora. The results show that TAGREAL achieves state-of-the-art performance on two benchmark datasets. We find that TAGREAL has superb performance even with limited training data, outperforming existing embedding-based, graph-based, and PLM-based methods.
  arXiv  Detail & Related papers  (2023-05-24T22:09:35Z)

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.