Are Large Language Models a Good Replacement of Taxonomies?
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the question of whether traditional knowledge graphs, specifically taxonomies, can be replaced by Large Language Models (LLMs) . This paper delves into the performance of LLMs over a wide range of taxonomies from common to specialized domains and at different levels within these taxonomies, from root to leaf . While the issue of replacing traditional taxonomies with LLMs is not entirely new, the comprehensive evaluation conducted in this paper, through the creation of a novel benchmark named TaxoGlimpse, provides valuable insights into the limitations of LLMs in capturing knowledge from specialized taxonomies and entities at the leaf level .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the hypothesis regarding whether Large Language Models (LLMs) can effectively replace traditional taxonomies, specifically focusing on their performance across a wide range of taxonomies from common to specialized domains and at different levels within these taxonomies, from root to leaf . The study investigates the ability of LLMs to capture knowledge in specialized taxonomies and leaf-level entities, aiming to determine if LLMs can adequately replace traditional taxonomies . The research addresses the question of whether LLMs are capable of performing well on common taxonomies and at levels that are common to people, while also exploring their limitations in capturing nuanced knowledge in specialized taxonomies and at leaf levels within taxonomies .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper introduces TaxoGlimpse, a novel benchmark structure that evaluates the performance of Large Language Models (LLMs) across various taxonomies, ranging from common to specialized domains and from root to leaf levels . The study systematically assesses eighteen state-of-the-art LLMs under three popular prompting settings: zero-shot, few-shot, and Chain-of-Thoughts, across different levels of ten representative taxonomies . The research addresses four key research questions and provides insights for future research opportunities for industrial users, LLM developers, and database researchers .
One of the main findings of the paper is that state-of-the-art LLMs demonstrate reliability in common domains like Shopping and General but lack sufficient domain knowledge in specialized areas such as Computer Science Research, Biology, Language, and Geography . This highlights the need for further research to enhance the domain knowledge coverage of LLMs, especially in specialized domains .
The paper also explores the performance of LLMs across different levels of taxonomies, indicating that the accuracy of LLMs tends to decrease as the taxonomy levels deepen, with most LLMs achieving around 80% accuracy in common shopping taxonomies . Additionally, the study investigates the influence of different prompting settings on LLM performance, including zero-shot, few-shot, and Chain-of-Thoughts settings. Few-shot prompting is shown to improve the performance of some LLMs, particularly in answering hierarchical structure discovery questions, while Chain-of-Thoughts prompting guides LLMs through complex reasoning questions, leading to more reasonable answers .
Overall, the paper provides a comprehensive evaluation of LLMs' performance on taxonomies, identifies areas for improvement in domain knowledge coverage, and suggests future research directions to combine LLMs with traditional taxonomies to create novel neural-symbolic taxonomies that leverage the strengths of both approaches . TaxoGlimpse, the benchmark structure introduced in the paper, offers a comprehensive evaluation of Large Language Models (LLMs) across various taxonomies, ranging from common to specialized domains and from root to leaf levels . This benchmark systematically assesses eighteen state-of-the-art LLMs under three popular prompting settings: zero-shot, few-shot, and Chain-of-Thoughts, providing insights for future research opportunities for industrial users, LLM developers, and database researchers .
One key advantage of TaxoGlimpse is its ability to evaluate LLMs' performance across different levels of taxonomies, shedding light on the reliability of LLMs in common domains versus specialized domains . The study reveals that while LLMs demonstrate proficiency in common domains like Shopping and General, they lack sufficient domain knowledge in specialized areas such as Computer Science Research, Biology, Language, and Geography .
Furthermore, TaxoGlimpse highlights the performance trends of LLMs from root to leaf levels in various taxonomies, indicating a root-to-leaf performance decline in most taxonomies . This insight underscores the need for further research to enhance LLMs' performance on leaf-level entities, presenting a promising direction for future ontology learning research .
The paper also explores the impact of different methods on improving LLMs' accuracy, such as increasing model sizes and providing domain-agnostic or domain-specific instruction fine-tuning . The findings suggest that larger model sizes can enhance LLMs' performance in certain taxonomies, emphasizing the importance of model size in improving accuracy .
Moreover, TaxoGlimpse investigates the influence of different prompting settings, such as Few-shot learning and Chain-of-Thoughts (CoT), on LLM performance . The study demonstrates that these prompting techniques can enhance LLMs' performance in answering hierarchical structure discovery questions and guiding LLMs through complex reasoning tasks, leading to more reasonable answers .
Overall, TaxoGlimpse stands out for its systematic evaluation of LLMs' performance on taxonomies, identification of performance trends across different levels, exploration of methods to enhance accuracy, and analysis of the impact of prompting settings on LLM performance, providing valuable insights for future research and development in the field of LLMs and taxonomies .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research works exist in the field of large language models and taxonomies. Noteworthy researchers in this area include Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Guihong Cao, Daxin Jiang, Ming Zhou , Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou , Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang , Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B Hashimoto , Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing , Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, Ji-Rong Wen , Yutaka Matsuo, Yusuke Iwasawa .
The key to the solution mentioned in the paper involves the creation of a new benchmark called TaxoGlimpse, which systematically covers taxonomies from common to specialized domains with in-depth root-to-leaf analysis. This benchmark addresses the challenges of the absence of a comprehensive benchmark, formulating an evaluation strategy for taxonomies, and the diversity of large language models .
How were the experiments in the paper designed?
The experiments in the paper were designed by selecting nine popular Large Language Model (LLM) series with eighteen models to comprehensively evaluate their performance . The experiments included various LLM series such as GPTs, Claude-3, Llama-2s, Llama-3s, Flan-T5s, Falcons, Vicunas, Mistrals, and LLMs4OL, each with different models and settings . The primary focus of the paper was to provide an initial analysis of LLMs' performance on taxonomies, and the experiments were conducted to evaluate the LLMs' knowledge and performance across different taxonomies and prompting settings . The experiments aimed to assess how well LLMs could capture knowledge from specialized taxonomies and entities at different levels, ranging from common to specialized domains and from root to leaf levels . The paper systematically evaluated the performances of the LLMs under three popular prompting settings: zero-shot, few-shot, and Chain-of-Thoughts, across various taxonomies and levels .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is TaxoGlimpse, which systematically covers taxonomies from common to specialized domains with in-depth root-to-leaf analysis . The code for the evaluation methods and analysis is open source and can be accessed on the GitHub repository associated with the study .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive evaluation of eighteen state-of-the-art Large Language Models (LLMs) across various taxonomies, ranging from common to specialized domains and from root to leaf levels . The experiments analyzed the reliability of LLMs in determining hierarchical structures in different taxonomies, revealing that LLMs perform well in common taxonomies like Shopping but exhibit decreased performance in specialized domains such as Biology . Additionally, the study investigated the performance of LLMs across different levels of taxonomies, showing a trend of progressively worse performance from root to leaf in most taxonomies .
Moreover, the experiments explored the impact of normal methods that enhance LLMs' reliability, indicating that increasing model sizes and adopting domain-agnostic fine-tuning may not consistently improve performance, while domain-specific instruction tuning leads to stable and significant performance enhancements . The study also examined how different prompting settings influence LLM performance, demonstrating minimal performance changes with certain prompting settings .
Overall, the experiments and results in the paper offer valuable insights into the performance of LLMs across diverse taxonomies, supporting the scientific hypotheses by providing empirical evidence of LLM behavior in different domains and levels of taxonomies . The comprehensive evaluation conducted in the study contributes significantly to understanding the capabilities and limitations of LLMs in replacing traditional taxonomies, highlighting areas for further research and development in this field.
What are the contributions of this paper?
The contributions of the paper include:
- Experimental results presented on Easy and MCQ datasets, showcasing the performance of Large Language Models (LLMs) under different prompting settings like Few-shot learning and Chain-of-Thoughts (CoT) .
- Introducing new prompting settings to evaluate LLMs' performance on taxonomies, such as Few-shot and CoT prompting techniques, which have been shown to enhance LLMs' performance .
- Systematically analyzing the performance of LLMs on different taxonomies from common to specialized domains, focusing on hierarchical structure discovery questions and reasoning ability improvements .
- Selecting nine popular LLM series with eighteen models for evaluation, including GPTs, Claude-3, Llama-2s, Llama-3s, Flan-T5s, Falcons, and Vicunas, to comprehensively assess the state-of-the-art LLMs .
- Designing question templates for True/False and Multiple-Choice Question (MCQ) types to evaluate LLMs' ability to discover hierarchical relationships in taxonomies across different domains .
- Investigating the reliability of LLMs in common domains like Shopping and General, highlighting their lack of domain knowledge in specialized domains such as Computer Science Research, Biology, Language, and Geography .
What work can be continued in depth?
To delve deeper into the research, further exploration can be conducted on the impact of domain-specific fine-tuning on the performance of Large Language Models (LLMs) in answering taxonomy structure questions. The study could focus on comparing the effectiveness of domain-agnostic fine-tuning versus domain-specific fine-tuning on different levels of taxonomies and their ability to enhance the understanding of question formats . Additionally, investigating the potential of introducing domain adaptation techniques to improve LLMs' performance on taxonomies could be a promising future direction to explore .