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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.00982 |
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| _version_ | 1866909125857771520 |
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| author | Yu, Xiao Lu, Yunan Yu, Zhou |
| author_facet | Yu, Xiao Lu, Yunan Yu, Zhou |
| contents | Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing toolkits allow users to quickly build such systems using off-the-shelf models, but they fall short in supporting researchers and developers to customize the model training, testing, and deployment process. We propose LocalRQA, an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. As a showcase, we build QA systems using online documentation obtained from Databricks and Faire's websites. We find 7B-models trained and deployed using LocalRQA reach a similar performance compared to using OpenAI's text-ada-002 and GPT-4-turbo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems Yu, Xiao Lu, Yunan Yu, Zhou Computation and Language Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing toolkits allow users to quickly build such systems using off-the-shelf models, but they fall short in supporting researchers and developers to customize the model training, testing, and deployment process. We propose LocalRQA, an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. As a showcase, we build QA systems using online documentation obtained from Databricks and Faire's websites. We find 7B-models trained and deployed using LocalRQA reach a similar performance compared to using OpenAI's text-ada-002 and GPT-4-turbo. |
| title | LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.00982 |