Evaluating Large Language Models on Graphs: Performance Insights and
Comparative Analysis
- URL: http://arxiv.org/abs/2308.11224v2
- Date: Sat, 9 Sep 2023 03:14:10 GMT
- Title: Evaluating Large Language Models on Graphs: Performance Insights and
Comparative Analysis
- Authors: Chang Liu, Bo Wu
- Abstract summary: We evaluate the capabilities of four Large Language Models (LLMs) in addressing several analytical problems with graph data.
We employ four distinct evaluation metrics: Correctness, Fidelity, and Rectification.
GPT models can generate logical and coherent results, outperforming alternatives in correctness.
- Score: 7.099257763803159
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have garnered considerable interest within both
academic and industrial. Yet, the application of LLMs to graph data remains
under-explored. In this study, we evaluate the capabilities of four LLMs in
addressing several analytical problems with graph data. We employ four distinct
evaluation metrics: Comprehension, Correctness, Fidelity, and Rectification.
Our results show that: 1) LLMs effectively comprehend graph data in natural
language and reason with graph topology. 2) GPT models can generate logical and
coherent results, outperforming alternatives in correctness. 3) All examined
LLMs face challenges in structural reasoning, with techniques like zero-shot
chain-of-thought and few-shot prompting showing diminished efficacy. 4) GPT
models often produce erroneous answers in multi-answer tasks, raising concerns
in fidelity. 5) GPT models exhibit elevated confidence in their outputs,
potentially hindering their rectification capacities. Notably, GPT-4 has
demonstrated the capacity to rectify responses from GPT-3.5-turbo and its own
previous iterations. The code is available at:
https://github.com/Ayame1006/LLMtoGraph.
Related papers
- GraphArena: Benchmarking Large Language Models on Graph Computational Problems [25.72820021030033]
"arms race" of Large Language Models (LLMs) demands novel, challenging, and diverse benchmarks to examine their progresses.
We introduce GraphArena, a benchmarking tool to evaluate models on graph computational problems using million-scale real-world graphs.
arXiv Detail & Related papers (2024-06-29T09:19:23Z) - GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets [19.329274124787858]
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP)
Recent studies have identified limitations in LLMs' ability to reason about graph-structured data.
We introduce GraphEval2000, the first comprehensive graph dataset, comprising 40 graph data structure problems along with 2000 test cases.
arXiv Detail & Related papers (2024-06-23T18:01:56Z) - Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction [35.01097297297534]
Existing evaluations of Large Language Models (LLMs) focus on problem-solving from the examinee perspective.
We define four evaluation tasks for error identification and correction along with a new dataset with annotated error types and steps.
Our principal findings indicate that GPT-4 outperforms all models, while open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source models GPT-3.5 and Gemini Pro.
arXiv Detail & Related papers (2024-06-02T14:16:24Z) - LLaGA: Large Language and Graph Assistant [73.71990472543027]
Large Language and Graph Assistant (LLaGA) is an innovative model to handle the complexities of graph-structured data.
LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks.
Our experiments show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model.
arXiv Detail & Related papers (2024-02-13T02:03:26Z) - The Earth is Flat? Unveiling Factual Errors in Large Language Models [89.94270049334479]
Large Language Models (LLMs) like ChatGPT are in various applications due to their extensive knowledge from pre-training and fine-tuning.
Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education.
We introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs.
arXiv Detail & Related papers (2024-01-01T14:02:27Z) - GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [7.218768686958888]
GraphLLM is a pioneering end-to-end approach that integrates graph learning models with Large Language Models.
Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM.
The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45%.
arXiv Detail & Related papers (2023-10-09T16:42:00Z) - Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications.
Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest.
This paper bifurcates such integrations into two predominant categories.
arXiv Detail & Related papers (2023-10-09T07:59:34Z) - Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency [127.97467912117652]
Large language models (LLMs) have exhibited remarkable ability in code generation.
However, generating the correct solution in a single attempt still remains a challenge.
We propose the Multi-Perspective Self-Consistency (MPSC) framework incorporating both inter- and intra-consistency.
arXiv Detail & Related papers (2023-09-29T14:23:26Z) - Benchmarking the Abilities of Large Language Models for RDF Knowledge
Graph Creation and Comprehension: How Well Do LLMs Speak Turtle? [0.0]
Large Language Models (LLMs) are advancing at a rapid pace, with significant improvements at natural language processing and coding tasks.
To evaluate the proficiency of various LLMs, we created a set of five tasks that probe their ability to parse, understand, analyze, and create knowledge graphs serialized in Turtle syntax.
The evaluation encompassed four commercially available LLMs - GPT-3.5, GPT-4, Claude 1.3, and Claude 2.0, as well as two freely accessible offline models, GPT4All Vicuna and GPT4All Falcon 13B.
arXiv Detail & Related papers (2023-09-29T10:36:04Z) - Can Language Models Solve Graph Problems in Natural Language? [51.28850846990929]
Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures.
We propose NLGraph, a benchmark of graph-based problem solving simulating in natural language.
arXiv Detail & Related papers (2023-05-17T08:29:21Z) - Prompting GPT-3 To Be Reliable [117.23966502293796]
This work decomposes reliability into four facets: generalizability, fairness, calibration, and factuality.
We find that GPT-3 outperforms smaller-scale supervised models by large margins on all these facets.
arXiv Detail & Related papers (2022-10-17T14:52:39Z)
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.