Summary Benchmarking Large Language Models for Knowledge Graph Creation arxiv.org
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The LLM-KG-Bench system assessed the proficiency of LLMs in working with formal languages for knowledge graph engineering, revealing varying strengths and weaknesses in different models.
Slides
Slide Presentation (9 slides)
Key Points
- Large Language Models (LLMs) have made advancements in natural language processing and coding tasks.
- LLMs' ability to work with formal languages, specifically in knowledge graph engineering, is still under investigation.
- A set of five tasks called LLM-KG-Bench was created to evaluate the proficiency of various LLMs in parsing, understanding, analyzing, and creating knowledge graphs using Turtle syntax.
- Commercial LLMs outperformed their predecessors in proficiency with Turtle language but struggled with adhering strictly to output formatting constraints.
- The benchmark tasks tested various aspects of LLMs' abilities, including graph handling, syntax error detection and correction, graph generation, link counting, and fact extraction from plaintext.
- Different LLMs performed differently in each task, with some showing strengths and weaknesses in specific areas.
- Challenges were identified in handling the size of graphs and generating Turtle-formatted output.
- Future work includes defining stricter tests, evaluating few-shot approaches, assessing performance using ntriples, and integrating LLMs with KGE-assistant plugins.
Summaries
24 word summary
The LLM-KG-Bench system evaluated LLMs' proficiency in working with formal languages for knowledge graph engineering. Different models showed strengths and weaknesses in benchmark tasks.
75 word summary
The proficiency of Large Language Models (LLMs) in working with formal languages for knowledge graph engineering was evaluated using the LLM-KG-Bench system. Four commercially available LLMs and two freely accessible offline models were tested. The latest commercial models performed better with the Turtle language but had weaknesses in adhering to output formatting constraints. Different models showed strengths and weaknesses in various benchmark tasks. Future work involves defining stricter tests and integrating LLMs with KGE-assistant plugins.
150 word summary
Large Language Models (LLMs) have been successful in natural language processing and coding tasks, but their proficiency in working with formal languages for knowledge graph engineering is still being explored. To evaluate their abilities, an automated evaluation system called LLM-KG-Bench was used to test four commercially available LLMs (GPT-3.5, GPT-4, Claude 1.3, and Claude 2.0) and two freely accessible offline models (GPT4All Vicuna and GPT4All Falcon 13B). The evaluation showed that the latest commercial models performed better than their predecessors in terms of proficiency with the Turtle language. However, there were weaknesses in adhering to output formatting constraints, which is crucial in knowledge graph engineering. The benchmark tasks included various aspects such as graph handling, error detection and correction, graph generation, link counting, and fact extraction from plaintext. Each model had its strengths and weaknesses in different tasks. Further work includes defining stricter tests and integrating LLMs with KGE-assistant plugins.
414 word summary
Large Language Models (LLMs) have made significant advancements in natural language processing and coding tasks. However, their ability to work with formal languages in knowledge graph engineering is still being investigated. To evaluate the proficiency of various LLMs in this area, a set of five tasks was created and integrated into an automated evaluation system called LLM-KG-Bench. The evaluation included 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.
The evaluation results showed that the latest commercial models outperformed their predecessors in terms of proficiency with the Turtle language. However, there was a weakness in adhering strictly to output formatting constraints, which is crucial in the context of knowledge graph engineering.
The benchmark tasks included T1: Find Connection in Small Turtle File, T2: Find Errors in Small Turtle File, T3: Create Sample Graphs, T4: Count Links in Person Graph, and T5: Create Knowledge Graph from Factsheet. These tasks tested various aspects of the LLMs' abilities, such as graph handling, error detection and correction, graph generation, link counting, and fact extraction from plaintext.
Claude-2.0 performed well in finding connections in small Turtle files, while GPT-4 occasionally added properties to the requested list. GPT-3.5 struggled with detecting errors in Turtle files. Claude-1.3 had difficulty generating accurate sample graphs, while GPT-4 used ellipses frequently for larger sizes. Falcon and Vicuna had limitations such as creating incomplete graphs or providing explanations instead of the requested output.
In counting links in a person graph, GPT-3.5 often confused outgoing links with incoming links, while GPT-4 performed well overall. Claude-2.0 and Falcon had similar confusion issues, and Vicuna showed some understanding for smaller sizes but failed for larger sizes. Challenges were also observed in handling the size of 6 persons for all commercial models.
In creating a knowledge graph from a factsheet, both GPT models outperformed Claude 1.3, with GPT-4 having a slightly better mean score. Claude 2.0 had the highest F1 scores and returned fewer unparseable documents compared to Claude 1.3 and GPT4. Falcon and Vicuna faced difficulties in generating Turtle-formatted output.
In conclusion, the evaluation showed promising results in the abilities of LLMs to work with Turtle syntax in knowledge graph engineering tasks. However, there is room for improvement in adhering to output formatting constraints and handling specific challenges in different tasks. Future work includes defining stricter tests, evaluating few-shot approaches, assessing performance using ntriples, and integrating LLMs with KGE-assistant plugins.
469 word summary
Large Language Models (LLMs) have made significant advancements in natural language processing and coding tasks. However, their ability to work with formal languages, specifically in knowledge graph engineering, is still under investigation. To evaluate the proficiency of various LLMs in this area, a set of five tasks was created to test their ability to parse, understand, analyze, and create knowledge graphs using Turtle syntax. These tasks were integrated into an automated evaluation system called LLM-KG-Bench. The evaluation included 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.
The evaluation results showed that the latest commercial models outperformed their predecessors in terms of proficiency with the Turtle language. However, there was an apparent weakness in adhering strictly to the output formatting constraints, which is a crucial requirement in the context of knowledge graph engineering.
The benchmark tasks included in the evaluation were T1: Find Connection in Small Turtle File, T2: Find Errors in Small Turtle File, T3: Create Sample Graphs, T4: Count Links in Person Graph, and T5: Create Knowledge Graph from Factsheet. These tasks tested various aspects of the LLMs' abilities, such as graph handling, syntax error detection and correction, graph generation, link counting, and fact extraction from plaintext.
The evaluation showed that Claude-2.0 performed well in the task of finding connections in small Turtle files, while GPT-4 occasionally added properties to the requested list. GPT-3.5 struggled with detecting errors in Turtle files and often claimed that the given file was correct. Claude-1.3 had difficulty generating sample graphs accurately, while GPT-4 used ellipses frequently for larger sizes. Falcon and Vicuna had their own limitations in these tasks, such as creating incomplete graphs or providing explanations instead of the requested output.
In the task of counting links in a person graph, GPT-3.5 often confused outgoing links with incoming links, while GPT-4 performed well overall. Claude-2.0 and Falcon had similar confusion issues, and Vicuna showed some understanding for smaller sizes but failed for larger sizes. The evaluation also revealed challenges in handling the size of 6 persons for all commercial models.
In the task of creating a knowledge graph from a factsheet, both GPT models outperformed Claude 1.3, with GPT-4 having a slightly better mean score. Claude 2.0 had the highest F1 scores and returned fewer unparseable documents compared to Claude 1.3 and GPT4. Falcon and Vicuna faced difficulties in generating Turtle-formatted output.
In conclusion, the evaluation showed promising results in the abilities of LLMs to work with Turtle syntax in knowledge graph engineering tasks. However, there is room for improvement in adhering to output formatting constraints and handling specific challenges in different tasks. Future work includes defining stricter tests, evaluating few-shot approaches, assessing performance using ntriples, and integrating LLMs with KGE-assistant plugins.