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Planning is a fundamental property of human intelligence.
Note on the sampling error of the difference between correlated proportions or percentages
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Effects of planning strategies on writing dynamics and final texts
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Advances in pre-training distributed word representations
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Language models are few-shot learners
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Reasoning about goals, steps, and temporal ordering with WikiHow
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Measuring and improving consistency in pretrained language models
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Knowledge-aware graph-enhanced GPT-2 for dialogue state tracking
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Could you give me a hint? Generating inference graphs for defeasible reasoning
Madaan, A., Rajagopal, D., Tandon, N., Yang, Y., and Hovy, E · 2021
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Two contrasting data annotation paradigms for subjective nlp tasks
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ExplaGraphs: An explanation graph generation task for structured commonsense reasoning
Saha, S., Yadav, P., Bauer, L., and Bansal, M · 2021
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proScript: Partially ordered scripts generation
Sakaguchi, K., Bhagavatula, C., Le Bras, R., Tandon, N., Clark, P., and Choi, Y · 2021
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Thinking like transformers
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Do as I can, not as I say: Grounding language in robotic affordances
AnnoLLM: Making large language models to be better crowdsourced annotators
He, X., Lin, Z., Gong, Y., Jin, A., Zhang, H., Lin, C., Jiao, J., Yiu, S. M., Duan, N., Chen, W., et al · 2023
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Huang, F., Kwak, H., and An, J · 2023
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Do language models have a common sense regarding time? Revisiting temporal commonsense reasoning in the era of large language models
Jain, R., Sojitra, D., Acharya, A., Saha, S., Jatowt, A., and Dandapat, S · 2023
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Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al · 2022
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Testing Large Language Models on compositionality and inference with phrase-level adjective-noun entailment
Bertolini, L., Weeds, J., and Weir, D · 2022
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Collins, K. M., Wong, C., Feng, J., Wei, M., and Tenenbaum, J. B · 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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Enhanced semantic representation learning for implicit discourse relation classification
Ma, Y., Zhu, J., and Liu, J · 2022
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Language models of code are few-shot commonsense learners
Madaan, A., Zhou, S., Alon, U., Yang, Y., and Neubig, G · 2022
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Measuring and narrowing the compositionality gap in language models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., and Lewis, M · 2022
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Lawless, C., Schoeffer, J., Le, L., Rowan, K., Sen, S., Hill, C. S., Suh, J., and Sarrafzadeh, B · 2023
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Text2motion: From natural language instructions to feasible plans
Lin, K., Agia, C., Migimatsu, T., Pavone, M., and Bohg, J · 2023
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Prompt position really matters in few-shot and zero-shot NLU tasks
Mao, J., Middleton, S. E., and Niranjan, M · 2023
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Self-consistency improves chain of thought reasoning in language models
Narang, S., Chowdhery, A., and Zhou, D · 2023
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OpenAI · 2023
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Graph-guided reasoning for multi-hop question answering in large language models
Park, J., Patel, A., Khan, O. Z., Kim, H. J., and Kim, J.-K · 2023
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Code Llama: Open foundation models for code
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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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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Can language models solve graph problems in natural language?
Wang, H., Feng, S., He, T., Tan, Z., Han, X., and Tsvetkov, Y · 2023
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Tram: Benchmarking temporal reasoning for large language models
Wang, Y. and Zhao, Y · 2023
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Translating natural language to planning goals with large-language models
Xie, Y., Yu, C., Zhu, T., Bai, J., Gong, Z., and Soh, H · 2023
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Coupling large language models with logic programming for robust and general reasoning from text
Yang, Z., Ishay, A., and Lee, J · 2023
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Natural language is all a graph needs
Ye, R., Zhang, C., Wang, R., Xu, S., and Zhang, Y · 2023
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Distilling script knowledge from large language models for constrained language planning
Yuan, S., Chen, J., Fu, Z., Ge, X., Shah, S., Jankowski, C., Xiao, Y., and Yang, D · 2023
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Code simulation challenges for large language models
La Malfa, E., Weinhuber, C., Torre, O., Lin, F., Cohn, A. G., Shadbolt, N., and Wooldridge, M · 2024
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Codemind: A framework to challenge large language models for code reasoning
Liu, C., Zhang, S. D., and Jabbarvand, R · 2024
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Consolidating trees of robotic plans generated using large language models to improve reliability
Sakib, M. S. and Sun, Y · 2024
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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