Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03023 | |
| Number of page(s) | 6 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403023 | |
| Published online | 06 April 2026 | |
- P. Lewis et al., Retrieval-augmented generation for knowledge-intensive NLP tasks, in Proc. NeurIPS (2020) [Google Scholar]
- W. Fan et al., A survey on RAG meeting LLMs: Towards retrieval-augmented large language models, arXiv preprint arXiv:2405.06211 (2024) [Google Scholar]
- P. Zhao et al., Retrieval-augmented generation for AI-generated content: A survey, arXiv preprint arXiv:2402.19473 (2024) [Google Scholar]
- Z. Hu et al., REVEAL: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory, in Proc. CVPR, 23369–23379 (2023) [Google Scholar]
- H. Liu et al., Visual instruction tuning, in Proc. NeurIPS (2023) [Google Scholar]
- X. Chu et al., MobileVLM: A fast, strong and open vision language assistant for mobile devices, arXiv preprint arXiv:2312.16886 (2023) [Google Scholar]
- A. Xenos et al., A simple baseline for knowledge-based visual question answering, in Proc. EMNLP (2023) [Google Scholar]
- P. Zhang et al., “TinyLlama: An open-source small language model,” arXiv:2401.02385, (2024) [Google Scholar]
- Qwen Team, “Qwen2.5: A comprehensive series of large language models,” arXiv:2409.12191, (2024) [Google Scholar]
- L. Zheng et al., Judging LLM-as-a-judge with MT-bench and chatbot arena, in Proc. NeurIPS (2023) [Google Scholar]
- S. Maekawa et al., Retrieval helps or hurts? A deeper dive into the efficacy of retrieval augmentation to language models, arXiv preprint arXiv:2402.13492 (2024) [Google Scholar]
- A. Asai et al., Self-RAG: Learning to retrieve, generate, and critique through self-reflection, in Proc. ICLR (2024) [Google Scholar]
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