Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913599377637376 |
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| author | Zheng, Yihang Li, Bo Lin, Zhenghao Luo, Yi Zhou, Xuanhe Lin, Chen Su, Jinsong Li, Guoliang Li, Shifu |
| author_facet | Zheng, Yihang Li, Bo Lin, Zhenghao Luo, Yi Zhou, Xuanhe Lin, Chen Su, Jinsong Li, Guoliang Li, Shifu |
| contents | The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database QA. To this end, we introduce DQABench, the first comprehensive database QA benchmark for LLMs. DQABench features an innovative LLM-based method to automate the generation, cleaning, and rewriting of evaluation dataset, resulting in over 200,000 QA pairs in English and Chinese, separately. These QA pairs cover a wide range of database-related knowledge extracted from manuals, online communities, and database instances. This inclusion allows for an additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database QA task. Furthermore, we propose a comprehensive LLM-based database QA testbed DQATestbed. This testbed is highly modular and scalable, with basic and advanced components such as Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Moreover, DQABench provides a comprehensive evaluation pipeline that computes various metrics throughout a standardized evaluation process to ensure the accuracy and fairness of the evaluation. We use DQABench to evaluate the database QA capabilities under the proposed testbed comprehensively. The evaluation reveals findings like (i) the strengths and limitations of nine LLM-based QA bots and (ii) the performance impact and potential improvements of various service components (e.g., QCR, RAG, TIG). Our benchmark and findings will guide the future development of LLM-based database QA research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation Zheng, Yihang Li, Bo Lin, Zhenghao Luo, Yi Zhou, Xuanhe Lin, Chen Su, Jinsong Li, Guoliang Li, Shifu Databases Artificial Intelligence The development of Large Language Models (LLMs) has revolutionized QA across various industries, including the database domain. However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database QA. To this end, we introduce DQABench, the first comprehensive database QA benchmark for LLMs. DQABench features an innovative LLM-based method to automate the generation, cleaning, and rewriting of evaluation dataset, resulting in over 200,000 QA pairs in English and Chinese, separately. These QA pairs cover a wide range of database-related knowledge extracted from manuals, online communities, and database instances. This inclusion allows for an additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database QA task. Furthermore, we propose a comprehensive LLM-based database QA testbed DQATestbed. This testbed is highly modular and scalable, with basic and advanced components such as Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Moreover, DQABench provides a comprehensive evaluation pipeline that computes various metrics throughout a standardized evaluation process to ensure the accuracy and fairness of the evaluation. We use DQABench to evaluate the database QA capabilities under the proposed testbed comprehensively. The evaluation reveals findings like (i) the strengths and limitations of nine LLM-based QA bots and (ii) the performance impact and potential improvements of various service components (e.g., QCR, RAG, TIG). Our benchmark and findings will guide the future development of LLM-based database QA research. |
| title | Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2409.04475 |