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Multi-agent interactions between Large Language Model (LLM) agents have shown major improvements on diverse reasoning tasks.
Yang, Z., Shou, L., Gong, M., Lin, W., and Jiang, D · 1910
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Support-vector networks
Cortes, C. and Vapnik, V · 1995
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Directions for multi-party human-computer interaction research
Kirchhoff, K. and Ostendorf, M · 2003
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Model compression
Buciluǎ, C., Caruana, R., and Niculescu-Mizil, A · 2006
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Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E · 2006
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Dialogue graph modeling for conversational machine reading
Ouyang, S., Zhang, Z., and Zhao, H · 2012
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Qin, L., Li, Z., Che, W., Ni, M., and Liu, T · 2012
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 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 multiple teacher networks
You, S., Xu, C., Xu, C., and Tao, D · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Discourse network analysis. policy debates as dynamic networks,[w:] jn victor, ah montgomery, m. lubell (red.), 2018
Leifeld, P · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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Dialoguegcn: A graph convolutional neural network for emotion recognition in conversation
Ghosal, D., Majumder, N., Poria, S., Chhaya, N., and Gelbukh, A · 2019
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Energy and policy considerations for deep learning in nlp
Strubell, E., Ganesh, A., and McCallum, A · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2019
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Explanations for CommonsenseQA: New Dataset and Models
Aggarwal, S., Mandowara, D., Agrawal, V., Khandelwal, D., Singla, P., and Garg, D · 2021
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Structure-aware abstractive conversation summarization via discourse and action graphs
Chen, J. and Yang, D · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., and Berant, J · 2021
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
Specializing smaller language models towards multi-step reasoning
Fu, Y., Peng, H., Ou, L., Sabharwal, A., and Khot, T · 2023
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Large language models are reasoning teachers
Ho, N., Schmid, L., and Yun, S.-Y · 2023
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Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Hsieh, C.-Y., Li, C.-L., Yeh, C.-k., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., and Pfister, T · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Symbolic chain-of-thought distillation: Small models can also “think” step-by-step
Li, L. H., Hessel, J., Yu, Y., Ren, X., Chang, K.-W., and Choi, Y · 2023
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Directed acyclic graph network for conversational emotion recognition
Shen, W., Wu, S., Yang, Y., and Quan, X · 2021
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Ethical and social risks of harm from language models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al · 2021
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Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
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LoRA: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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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
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Symbolic knowledge distillation: from general language models to commonsense models
West, P., Bhagavatula, C., Hessel, J., Hwang, J., Jiang, L., Le Bras, R., Lu, X., Welleck, S., and Choi, Y · 2022
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Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., Yang, Y., Tu, Z., and Shi, S · 2023
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Tinygsm: achieving > > 80% on gsm8k with small language models
Liu, B., Bubeck, S., Eldan, R., Kulkarni, J., Li, Y., Nguyen, A., Ward, R., and Zhang, Y · 2023
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Teaching small language models to reason
Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., and Severyn, A · 2023
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Orca 2: Teaching small language models how to reason
Mitra, A., Del Corro, L., Mahajan, S., Codas, A., Simoes, C., Agarwal, S., Chen, X., Razdaibiedina, A., Jones, E., Aggarwal, K., et al · 2023
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Orca: Progressive learning from complex explanation traces of gpt-4
Mukherjee, S., Mitra, A., Jawahar, G., Agarwal, S., Palangi, H., and Awadallah, A · 2023
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Can language models teach weaker agents? teacher explanations improve students via personalization
Saha, S., Hase, P., and Bansal, M · 2023
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Distilling reasoning capabilities into smaller language models
Shridhar, K., Stolfo, A., and Sachan, M · 2023
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Stanford alpaca: An instruction-following llama model, 2023
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Self-instruct: Aligning language models with self-generated instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H · 2023
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Multi-party chat: Conversational agents in group settings with humans and models
Wei, J., Shuster, K., Szlam, A., Weston, J., Urbanek, J., and Komeili, M · 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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Lumos: Learning agents with unified data, modular design, and open-source llms
Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., and Lin, B. Y · 2023
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