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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/2410.20598 |
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| _version_ | 1866915005372301312 |
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| author | Wang, Zihan Ge, Xuri Jose, Joemon M. Yu, Haitao Ma, Weizhi Ren, Zhaochun Xin, Xin |
| author_facet | Wang, Zihan Ge, Xuri Jose, Joemon M. Yu, Haitao Ma, Weizhi Ren, Zhaochun Xin, Xin |
| contents | Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and performance of large language model (LLM)-based applications. However, with the comprehensive application of RAG, more and more problems and limitations have been identified, thus urgently requiring further fundamental exploration to improve current RAG frameworks. This workshop aims to explore in depth how to conduct refined and reliable RAG for downstream AI tasks.
To this end, we propose to organize the first R3AG workshop at SIGIR-AP 2024 to call for participants to re-examine and formulate the basic principles and practical implementation of refined and reliable RAG. The workshop serves as a platform for both academia and industry researchers to conduct discussions, share insights, and foster research to build the next generation of RAG systems. Participants will engage in discussions and presentations focusing on fundamental challenges, cutting-edge research, and potential pathways to improve RAG. At the end of the workshop, we aim to have a clearer understanding of how to improve the reliability and applicability of RAG with more robust information retrieval and language generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation Wang, Zihan Ge, Xuri Jose, Joemon M. Yu, Haitao Ma, Weizhi Ren, Zhaochun Xin, Xin Information Retrieval Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and performance of large language model (LLM)-based applications. However, with the comprehensive application of RAG, more and more problems and limitations have been identified, thus urgently requiring further fundamental exploration to improve current RAG frameworks. This workshop aims to explore in depth how to conduct refined and reliable RAG for downstream AI tasks. To this end, we propose to organize the first R3AG workshop at SIGIR-AP 2024 to call for participants to re-examine and formulate the basic principles and practical implementation of refined and reliable RAG. The workshop serves as a platform for both academia and industry researchers to conduct discussions, share insights, and foster research to build the next generation of RAG systems. Participants will engage in discussions and presentations focusing on fundamental challenges, cutting-edge research, and potential pathways to improve RAG. At the end of the workshop, we aim to have a clearer understanding of how to improve the reliability and applicability of RAG with more robust information retrieval and language generation. |
| title | R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2410.20598 |