Retrieving Multimodal Information for Augmented Generation: A Survey

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372

Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101

PaLM: Scaling Language Modeling with Pathways

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590

Zero-shot text-to-image generation

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv · doi:10.48550/arxiv.2102.12092 · Cited by 2,417

Multimodal Transformer for Unaligned Multimodal Language Sequences

Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1656 · Cited by 2,259

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.685 · Cited by 1,495

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · arXiv · doi:10.48550/arxiv.2109.00859 · Cited by 1,495

Evaluating Large Language Models Trained on Code

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436

Multimodal biomedical AI

Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023) · arXiv · doi:10.48550/arxiv.2302.04761 · Cited by 1,150

Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Li, J., Li, D., Savarese, S., & Hoi, S. (2023) · arXiv · doi:10.48550/arxiv.2301.12597 · Cited by 908

A survey on deep learning for multimodal data fusion

Gao, J., Li, P., Chen, Z., & Zhang, J. (2020) · Neural Computation · doi:10.1162/neco_a_01273 · Cited by 821

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.08239 · Cited by 778

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.499 · Cited by 673

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · arXiv · doi:10.48550/arxiv.2212.10509 · Cited by 349

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.557 · Cited by 349

Shaping program repair space with existing patches and similar code

Jiang, J., Xiong, Y., Zhang, H., Gao, Q., & Chen, X. (2018) · Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3213846.3213871 · Cited by 329

Are NLP Models really able to Solve Simple Math Word Problems?

Patel, A., Bhattamishra, S., & Goyal, N. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.168 · Cited by 307

WebGPT: Browser-assisted question-answering with human feedback

Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.09332 · Cited by 272

Language Is Not All You Need: Aligning Perception with Language Models

Huang, S., Dong, L., Wang, W., Hao, Y., Singhal, S., Ma, S., Lv, T., Cui, L., Mohammed, O. K., Patra, B., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.14045 · Cited by 235

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.00598 · Cited by 205

Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning

Nashid, N., Sintaha, M., & Mesbah, A. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00205 · Cited by 178

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Weston, J., Dinan, E., & Miller, A. (2018) · Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI · doi:10.18653/v1/w18-5713 · Cited by 165

PAL: Program-aided Language Models

Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022) · arXiv · doi:10.48550/arxiv.2211.10435 · Cited by 155

KAT: A Knowledge Augmented Transformer for Vision-and-Language

Gui, L., Wang, B., Huang, Q., Hauptmann, A., Bisk, Y., & Gao, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.70 · Cited by 130

A Retrieve-and-Edit Framework for Predicting Structured Outputs

Hashimoto, T. B., Guu, K., Oren, Y., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1812.01194 · Cited by 126

Repair Is Nearly Generation: Multilingual Program Repair with LLMs

Joshi, H., Cambronero Sanchez, J., Gulwani, S., Le, V., Verbruggen, G., & Radiček, I. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25642 · Cited by 126

Simple Entity-Centric Questions Challenge Dense Retrievers

Sciavolino, C., Zhong, Z., Lee, J., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.496 · Cited by 98

Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.168 · Cited by 81

Binding Language Models in Symbolic Languages

Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.02875 · Cited by 53

RACE: Retrieval-augmented Commit Message Generation

Shi, E., Wang, Y., Tao, W., Du, L., Zhang, H., Han, S., Zhang, D., & Sun, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.372 · Cited by 42

MusCaps: Generating Captions for Music Audio

Manco, I., Benetos, E., Quinton, E., & Fazekas, G. (2021) · 2021 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533461 · Cited by 36

DocPrompting: Generating Code by Retrieving the Docs

Zhou, S., Alon, U., Xu, F. F., Wang, Z., Jiang, Z., & Neubig, G. (2022) · arXiv · doi:10.48550/arxiv.2207.05987 · Cited by 33

Relational memory-augmented language models

Liu, Q., Yogatama, D., & Blunsom, P. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00476 · Cited by 23

Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation

Kang, M., Kwak, J. M., Baek, J., & Hwang, S. J. (2023) · arXiv · doi:10.48550/arxiv.2305.18846 · Cited by 13

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

Clement, C., Lu, S., Liu, X., Tufano, M., Drain, D., Duan, N., Sundaresan, N., & Svyatkovskiy, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.387 · Cited by 12

Retrieval-Augmented Generation for AI-Generated Content: A Survey

Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., Jiang, J., & Cui, B. (2024) · arXiv · doi:10.48550/arxiv.2402.19473 · Cited by 240

Is GPT-3 a Good Data Annotator?

Ding, B., Qin, C., Liu, L., Chia, Y. K., Li, B., Joty, S., & Bing, L. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.626 · Cited by 149

Combating misinformation in the age of LLMs: Opportunities and challenges

Chen, C., & Shu, K. (2024) · AI Magazine · doi:10.1002/aaai.12188 · Cited by 148

A survey on knowledge-oriented retrieval-augmented generation

Cheng, M., Luo, Y., Ouyang, J., Liu, Q., Liu, H., Li, L., Yu, S., Zhang, B., Cao, J., Ma, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.10677 · Cited by 140

Graph Retrieval-Augmented Generation for Large Language Models: A Survey

Procko, T. T., & Ochoa, O. (2024) · 2024 Conference on AI, Science, Engineering, and Technology (AIxSET) · doi:10.1109/aixset62544.2024.00030 · Cited by 125