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Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs).
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Schmidt, E · 2014
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M. I., and Abbeel, P · 2016
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., and Mordatch, I · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., and Manning, C. D · 2018
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Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A. P., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M., Dai, A. M., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., and Aizawa, A · 2020
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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., Jiang, X., Cobbe, K., Eloundou, T., Krueger, G., Button, K., Knight, M., Chess, B., and Schulman, J · 2021
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Unsupervised dense information retrieval with contrastive learning
Izacard, G., Caron, M., Hosseini, L., Riedel, S., Bojanowski, P., Joulin, A., and Grave, E · 2022
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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., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R · 2022
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MuSiQue: Multihop questions via single-hop question composition
Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A · 2022
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Retrieval-augmented generation for large language models: A survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Guo, Q., Wang, M., and Wang, H · 2023
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Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
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Towards general text embeddings with multi-stage contrastive learning
Li, Z., Zhang, X., Zhang, Y., Long, D., Xie, P., and Zhang, M · 2023
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Query rewriting in retrieval-augmented large language models
Ma, X., Gong, Y., He, P., Zhao, H., and Duan, N · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., and Hajishirzi, H · 2023
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Self-knowledge guided retrieval augmentation for large language models
Wang, Y., Li, P., Sun, M., and Liu, Y · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
Cited alongside, same era.
Gpt-4o system card
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Gemma: Open models based on gemini research and technology
Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., Tafti, P., Hussenot, L., Chowdhery, A., Roberts, A., Barua, A., Botev, A., Castro-Ros, A., Slone, A., Héliou, A., Tacchetti, A., Bulanova, A., Paterson, A., Tsai, B., Shahriari, B., Lan, C. L., Choquette-Choo, C. A., Crepy, C., Cer, D., Ippolito, D., Reid, D., Buchatskaya, E., Ni, E., Noland, E., Yan, G., Tucker, G., Muraru, G., Rozhdestvenskiy, G., Michalewski, H., Tenney, I., Grishchenko, I., Austin, J., Keeling, J., Labanowski, J., Lespiau, J., Stanway, J., Brennan, J., Chen, J., Ferret, J., Chiu, J., and et al · 2024
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Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku
Anthropic · 2024
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Asai, A., Wu, Z., Wang, Y., Sil, A., and Hajishirzi, H · 2024
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RQ-RAG: learning to refine queries for retrieval augmented generation
Chan, C., Xu, C., Yuan, R., Luo, H., Xue, W., Guo, Y., and Fu, J · 2024
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Alphamath almost zero: Process supervision without process
Chen, G., Liao, M., Li, C., and Fan, K · 2024
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Step-level value preference optimization for mathematical reasoning
Chen, G., Liao, M., Li, C., and Fan, K · 2024
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The llama 3 herd of models
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., Goyal, A., Hartshorn, A., Yang, A., Mitra, A., Sravankumar, A., Korenev, A., Hinsvark, A., Rao, A., Zhang, A., Rodriguez, A., Gregerson, A., Spataru, A., Rozière, B., Biron, B., Tang, B., Chern, B., Caucheteux, C., Nayak, C., Bi, C., Marra, C., McConnell, C., Keller, C., Touret, C., Wu, C., Wong, C., Ferrer, C. C., Nikolaidis, C., Allonsius, D., Song, D., Pintz, D., Livshits, D., Esiobu, D., Choudhary, D., Mahajan, D., Garcia-Olano, D., Perino, D., Hupkes, D., Lakomkin, E., AlBadawy, E., Lobanova, E., Dinan, E., Smith, E. M., Radenovic, F., Zhang, F., Synnaeve, G., Lee, G., Anderson, G. L., Nail, G., Mialon, G., Pang, G., Cucurell, G., Nguyen, H., Korevaar, H., Xu, H., Touvron, H., Zarov, I., Ibarra, I. A., Kloumann, I. M., Misra, I., Evtimov, I., Copet, J., Lee, J., Geffert, J., Vranes, J., Park, J., Mahadeokar, J., Shah, J., van der Linde, J., Billock, J., Hong, J., Lee, J., Fu, J., Chi, J., Huang, J., Liu, J., Wang, J., Yu, J., Bitton, J., Spisak, J., Park, J., Rocca, J., Johnstun, J., Saxe, J., Jia, J., Alwala, K. V., Upasani, K., Plawiak, K., Li, K., Heafield, K., Stone, K., and et al · 2024
Cited alongside, same era.
A survey on RAG meeting llms: Towards retrieval-augmented large language models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T., and Li, Q · 2024
Cited alongside, same era.
Qiao, Z., Ye, W., Jiang, Y., Mo, T., Xie, P., Li, W., Huang, F., and Zhang, S · 2024
Later among the works it cites.
REPLUG: retrieval-augmented black-box language models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., and Yih, W · 2024
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Small models, big insights: Leveraging slim proxy models to decide when and what to retrieve for llms
Tan, J., Dou, Z., Zhu, Y., Guo, P., Fang, K., and Wen, J · 2024
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Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries
Tang, Y. and Yang, Y · 2024
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Freshllms: Refreshing large language models with search engine augmentation
Vu, T., Iyyer, M., Wang, X., Constant, N., Wei, J. W., Wei, J., Tar, C., Sung, Y., Zhou, D., Le, Q. V., and Luong, T · 2024
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Sequential asynchronous action coordination in multi-agent systems: A stackelberg decision transformer approach
Zhang, B., Mao, H., Li, L., Xu, Z., Li, D., Zhao, R., and Fan, G · 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., and Ma, Y · 2024
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Trustworthiness in retrieval-augmented generation systems: A survey
Zhou, Y., Liu, Y., Li, X., Jin, J., Qian, H., Liu, Z., Li, C., Dou, Z., Ho, T., and Yu, P. S · 2024
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A survey of multi-agent deep reinforcement learning with communication
Zhu, C., Dastani, M., and Wang, S · 2024
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Learning evolving tools for large language models
Chen, G., Zhang, Z., Cong, X., Guo, F., Wu, Y., Lin, Y., Feng, W., and Wang, Y · 2025
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Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al · 2025
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InstructRAG: Instructing retrieval-augmented generation via self-synthesized rationales
Wei, Z., Chen, W.-L., and Meng, Y · 2025
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