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Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations.
Microsoft coco: Common objects in context
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Exploring models and data for image question answering
M. Ren, R. Kiros, and R. Zemel · 2015
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Fine-tuning language models from human preferences
N. Ziegler, D. M .and Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, et al · 2020
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Learning transferable visual models from natural language supervision
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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Survey of hallucination in natural language generation
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
W. Dai, J. Li, D. LI, A. Tiong, J. Zhao, W. Wang, B. Li, P. N Fung, and S. Hoi · 2023
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Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J. B. Alayrac, J. Yu, R. Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
S. Min, K. Krishna, X. Lyu, M. Lewis, W. Yih, P. W. Koh, M. Iyyer, L. Zettlemoyer, and H. Hajishirzi · 2023
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Ragas: Automated evaluation of retrieval augmented generation
Sigmoid loss for language image pre-training
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer · 2023
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Visual instruction tuning
H. Liu, C. Li, Q. Wu, and Y. J. Lee · 2024
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Vila: On pre-training for visual language models
J. Lin, H. Yin, W. Ping, P. Molchanov, M. Shoeybi, and S. Han · 2024
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Y. S. Chuang, L. Qiu, C. Y. Hsieh, R. Krishna, Y. Kim, and J. Glass · 2024
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Mars: Meaning-aware response scoring for uncertainty estimation in generative llms
Y. F. Bakman, D. N. Yaldiz, B. Buyukates, C. Tao, D. Dimitriadis, and S. Avestimehr · 2024
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S. Es, J. James, L. Espinosa-Anke, and S. Schockaert · 2023
Cited alongside, same era.
Faithscore: Evaluating hallucinations in large vision-language models
L. Jing, R. Li, Y. Chen, M. Jia, and X. Du · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. A. Lachaux, T. Lacroix, B. Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Detecting and preventing hallucinations in large vision language models
A. Gunjal, J. Yin, and E. Bas · 2024
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C. Jiang, W. Ye, M. Dong, H. Jia, H. Xu, M. Yan, J. Zhang, and S. Zhang · 2024
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