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In the realm of embodied artificial intelligence, the reasoning capabilities of Large Language Models (LLMs) play a pivotal role.
A Complexity Measure
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Language Models are Few-Shot Learners
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A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
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Training Verifiers to Solve Math Word Problems
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Measuring Mathematical Problem Solving With the MATH Dataset
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Measuring Association Between Labels and Free-Text Rationales
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KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
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PAL: Program-aided Language Models
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Towards Reasoning in Large Language Models: A Survey
Huang, J.; and Chang, K. C. 2022 · 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.; Sermanet, P.; Jackson, T.; Brown, N.; Luu, L.; Levine, S.; Hausman, K.; and Ichter, B. 2022 · 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 · 2022
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Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Suzgun, M.; Scales, N.; Schärli, N.; Gehrmann, S.; Tay, Y.; Chung, H. W.; Chowdhery, A.; Le, Q. V.; Chi, E. H.; Zhou, D.; and Wei, J. 2022 · 2022
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Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language
Wang, X.; Li, S.; and Ji, H. 2022 · 2022
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Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Hu, Y.; Yang, H.; Lin, Z.; and Zhang, M. 2023 · 2023
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VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
Huang, W.; Wang, C.; Zhang, R.; Li, Y.; Wu, J.; and Fei-Fei, L. 2023 · 2023
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MathPrompter: Mathematical Reasoning using Large Language Models
Imani, S.; Du, L.; and Shrivastava, H. 2023 · 2023
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CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
Li, P.; Sun, T.; Tang, Q.; Yan, H.; Wu, Y.; Huang, X.; and Qiu, X. 2023 · 2023
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The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code
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Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; Chi, E. H.; Le, Q. V.; and Zhou, D. 2022 · 2022
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LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning
Yang, Z.; Qin, J.; Chen, J.; Lin, L.; and Liang, X. 2022 · 2022
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The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning
Zhang, H.; Zhang, Y.; Li, L. E.; and Xing, E. P. 2022 · 2022
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Falcon-40B: an open large language model with state-of-the-art performance
Almazrouei, E.; Alobeidli, H.; Alshamsi, A.; Cappelli, A.; Cojocaru, R.; Debbah, M.; Goffinet, E.; Heslow, D.; Launay, J.; Malartic, Q.; Noune, B.; Pannier, B.; and Penedo, G. 2023 · 2023
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Anil, R.; Dai, A. M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P.; Chen, Z.; Chu, E.; Clark, J. H.; Shafey, L. E.; and et al. 2023 · 2023
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CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Bi, Z.; Chen, J.; Jiang, Y.; Xiong, F.; Guo, W.; Chen, H.; and Zhang, N. 2023 · 2023
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Code Alpaca: An Instruction-following LLaMA model for code generation
Chaudhary, S. 2023 · 2023
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Tele-Knowledge Pre-training for Fault Analysis
Chen, Z.; Zhang, W.; Huang, Y.; Chen, M.; Geng, Y.; Yu, H.; Bi, Z.; Zhang, Y.; Yao, Z.; Song, W.; Wu, X.; Yang, Y.; Chen, M.; Lian, Z.; Li, Y.; Cheng, L.; and Chen, H. 2023 · 2023
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Liu, X.; Yin, D.; Zhang, C.; Feng, Y.; and Zhao, D. 2023 · 2023
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Orca: Progressive Learning from Complex Explanation Traces of GPT-4
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OpenAI. 2023 · 2023
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Why think step-by-step? Reasoning emerges from the locality of experience
Prystawski, B.; and Goodman, N. D. 2023 · 2023
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Reasoning with Language Model Prompting: A Survey
Qiao, S.; Ou, Y.; Zhang, N.; Chen, X.; Yao, Y.; Deng, S.; Tan, C.; Huang, F.; and Chen, H. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; Rodriguez, A.; Joulin, A.; Grave, E.; and Lample, G. 2023 · 2023
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Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions?
Varshney, N.; Parmar, M.; Patel, N.; Handa, D.; Sarkar, S.; Luo, M.; and Baral, C. 2023 · 2023
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Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
Xie, Y.; Kawaguchi, K.; Zhao, Y.; Zhao, X.; Kan, M.; He, J.; and Xie, Q. 2023 · 2023
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Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Yuan, Z.; Yuan, H.; Li, C.; Dong, G.; Tan, C.; and Zhou, C. 2023 · 2023
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A Survey of Large Language Models
Zhao, W. X.; Zhou, K.; Li, J.; Tang, T.; Wang, X.; Hou, Y.; Min, Y.; Zhang, B.; Zhang, J.; Dong, Z.; Du, Y.; Yang, C.; Chen, Y.; Chen, Z.; Jiang, J.; Ren, R.; Li, Y.; Tang, X.; Liu, Z.; Liu, P.; Nie, J.; and Wen, J. 2023 · 2023
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PaD: Program-aided Distillation Specializes Large Models in Reasoning
Zhu, X.; Qi, B.; Zhang, K.; Long, X.; and Zhou, B. 2023 · 2023
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Are NLP Models really able to Solve Simple Math Word Problems?
Patel, A.; Bhattamishra, S.; and Goyal, N. 2021 · 2094
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