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The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities.
Extending cognitive architecture with episodic memory
Nuxoll, A. M. and Laird, J. E · 2007
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Generative deep neural networks for dialogue: A short review
Serban, I. V., Lowe, R., Charlin, L., and Pineau, J · 2016
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 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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Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Learning from richer human guidance: Augmenting comparison-based learning with feature queries
Basu, C., Singhal, M., and Dragan, A. D · 2018
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Multi-agent systems: A survey
Dorri, A., Kanhere, S. S., and Jurdak, R · 2018
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Fastspeech: Fast, robust and controllable text to speech
Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., and Liu, T.-Y · 2019
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SMILES-BERT: large scale unsupervised pre-training for molecular property prediction
Wang, S., Guo, Y., Wang, Y., Sun, H., and Huang, J · 2019
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Towards a human-like open-domain chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al · 2020
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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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Improving contextual language models for response retrieval in multi-turn conversation
Lu, J., Ren, X., Ren, Y., Liu, A., and Xu, Z · 2020
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Learning to incentivize other learning agents
Yang, J., Li, A., Farajtabar, M., Sunehag, P., Hughes, E., and Zha, H · 2020
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Visualgpt: Data-efficient image captioning by balancing visual input and linguistic knowledge from pretraining
Chen, J., Guo, H., Yi, K., Li, B., and Elhoseiny, M · 2021
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Graph infomax adversarial learning for treatment effect estimation with networked observational data
Chu, Z., Rathbun, S. L., and Li, S · 2021
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Explicable reward design for reinforcement learning agents
Devidze, R., Radanovic, G., Kamalaruban, P., and Singla, A · 2021
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Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech
Kim, J., Kong, J., and Son, J · 2021
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Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer
Mehta, S. and Rastegari, M · 2021
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Learning rewards from linguistic feedback
Sumers, T. R., Ho, M. K., Hawkins, R. D., Narasimhan, K., and Griffiths, T. L · 2021
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Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2021
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al · 2022
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Inner monologue: Embodied reasoning through planning with language models
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al · 2022
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Large language models are zero-shot reasoners, 2022
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Inferring rewards from language in context
Lin, J., Fried, D., Klein, D., and Dragan, A · 2022
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Memory-assisted prompt editing to improve gpt-3 after deployment
Madaan, A., Tandon, N., Clark, P., and Yang, Y · 2022
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Progen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J., Weinstein, E. N., Naik, N., and Madani, A · 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
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Workshop, B., Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., et al · 2022
Cited alongside, same era.
Language model self-improvement by reinforcement learning contemplation
Pang, J., Wang, P., Li, K., Chen, X., Xu, J., Zhang, Z., and Yu, Y · 2023
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Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S · 2023
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Self-driven grounding: Large language model agents with automatical language-aligned skill learning
Peng, S., Hu, X., Yi, Q., Zhang, R., Guo, J., Huang, D., Tian, Z., Chen, R., Du, Z., Guo, Q., Chen, Y., and Li, L · 2023
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Communicative agents for software development
Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z., and Sun, M · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
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Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
Cited alongside, same era.
Syntaspeech: syntax-aware generative adversarial text-to-speech
Ye, Z., Zhao, Z., Ren, Y., and Wu, F · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Cited alongside, same era.
Rest meets react: Self-improvement for multi-step reasoning LLM agent
Aksitov, R., Miryoosefi, S., Li, Z., Li, D., Babayan, S., Kopparapu, K., Fisher, Z., Guo, R., Prakash, S., Srinivasan, P., Zaheer, M., Yu, F. X., and Kumar, S · 2023
Cited alongside, same era.
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al · 2023
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., Lehmann, T., Podstawski, M., Niewiadomski, H., Nyczyk, P., et al · 2023
Cited alongside, same era.
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., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2023
Cited alongside, same era.
Dagan, G., Keller, F., and Lascarides, A · 2023
Cited alongside, same era.
Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, X., Qian, B., et al · 2023
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Sayplan: Grounding large language models using 3d scene graphs for scalable task planning
Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., and Suenderhauf, N · 2023
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Efficient RLHF: reducing the memory usage of PPO
Santacroce, M., Lu, Y., Yu, H., Li, Y., and Shen, Y · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Memory augmented large language models are computationally universal
Schuurmans, D · 2023
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Algorithm of thoughts: Enhancing exploration of ideas in large language models
Sel, B., Al-Tawaha, A., Khattar, V., Wang, L., Jia, R., and Jin, M · 2023
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Clever hans or neural theory of mind? stress testing social reasoning in large language models
Shapira, N., Levy, M., Alavi, S. H., Zhou, X., Choi, Y., Goldberg, Y., Sap, M., and Shwartz, V · 2023
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. R., and Yao, S · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Song, C. H., Wu, J., Washington, C., Sadler, B. M., Chao, W.-L., and Su, Y · 2023
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Cognitive architectures for language agents
Sumers, T. R., Yao, S., Narasimhan, K., and Griffiths, T. L · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agents
Sun, W., Yan, L., Ma, X., Wang, S., Ren, P., Chen, Z., Yin, D., and Ren, Z · 2023
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Better language models of code through self-improvement
To, H. Q., Bui, N. D. Q., Guo, J. L. C., and Nguyen, T. N · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Multi-level protein structure pre-training via prompt learning
Wang, Z., Zhang, Q., Hu, S., Yu, H., Jin, X., Gong, Z., and Chen, H · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., and Wang, C · 2023
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The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al · 2023
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Openagents: An open platform for language agents in the wild
Xie, T., Zhou, F., Cheng, Z., Shi, P., Weng, L., Liu, Y., Hua, T. J., Zhao, J., Liu, Q., Liu, C., et al · 2023
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Rewoo: Decoupling reasoning from observations for efficient augmented language models
Xu, B., Peng, Z., Lei, B., Mukherjee, S., Liu, Y., and Xu, D · 2023
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Appagent: Multimodal agents as smartphone users
Yang, Z., Liu, J., Han, Y., Chen, X., Huang, Z., Fu, B., and Yu, G · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Memorybank: Enhancing large language models with long-term memory
Zhong, W., Guo, L., Gao, Q., and Wang, Y · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
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Llm-guided multi-view hypergraph learning for human-centric explainable recommendation
Chu, Z., Wang, Y., Cui, Q., Li, L., Chen, W., Li, S., Qin, Z., and Ren, K · 2024
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Large language model based multi-agents: A survey of progress and challenges
Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., and Zhang, X · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J · 2024
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