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Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to external modi operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities.
The empirical case for two systems of reasoning
Sloman, S. A · 1996
Earlier work this paper cites.
Working memory: looking back and looking forward
Baddeley, A · 2003
Earlier work this paper cites.
Task switching
Monsell, S · 2003
Earlier work this paper cites.
The Cambridge handbook of thinking and reasoning
Holyoak, K. J. and Morrison, R. G · 2005
Earlier work this paper cites.
A comparison of most-to-least and least-to-most prompting on the acquisition of solitary play skills
Libby, M. E., Weiss, J. S., Bancroft, S., and Ahearn, W. H · 2008
Earlier work this paper cites.
Thinking, fast and slow
Kahneman, D · 2011
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
The carbon impact of artificial intelligence
Dhar, P · 2020
Earlier work this paper cites.
A singular value thresholding algorithm for order estimation
Al-Tawaha, A. S., Aljanaideh, K., and Alshorman, A · 2021
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Earlier work this paper cites.
Glsdc based parameter estimation algorithm for a pmsm model
Sel, A., Sel, B., and Kasnakoglu, C · 2021
Earlier work this paper cites.
Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale
Aminabadi, R. Y., Rajbhandari, S., Awan, A. A., Li, C., Li, D., Zheng, E., Ruwase, O., Smith, S., Zhang, M., Rasley, J., et al · 2022
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
Earlier work this paper cites.
Qualitative mechanical problem-solving by artificial agents:: Further progress, under psychometric ai
Banerjee, S., Bringsjord, S., Giancola, M., and Govindarajulu, N. S · 2022
Earlier work this paper cites.
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., et al · 2022
Earlier work this paper cites.
Compositional Semantic Parsing with Large Language Models
Drozdov, A., Schärli, N., Akyürek, E., Scales, N., Song, X., Chen, X., Bousquet, O., and Zhou, D · 2022
Earlier work this paper cites.
When is psychology research useful in artificial intelligence? a case for reducing computational complexity in problem solving
Helie, S. and Pizlo, Z · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Huang, J. and Chang, K. C.-C · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al · 2022
Cited alongside, same era.
A cmdp-within-online framework for meta-safe reinforcement learning
Khattar, V., Ding, Y., Sel, B., Lavaei, J., and Jin, M · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Cited alongside, same era.
Frugalgpt: How to use large language models while reducing cost and improving performance
Chen, L., Zaharia, M., and Zou, J · 2023
Closest in time.
Tighter bounds on the expressivity of transformer encoders
Chiang, D., Cholak, P., and Pillay, A · 2023
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Towards revealing the mystery behind chain of thought: a theoretical perspective
Feng, G., Gu, Y., Zhang, B., Ye, H., He, D., and Wang, L · 2023
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Winning the citylearn challenge: adaptive optimization with evolutionary search under trajectory-based guidance
Khattar, V. and Jin, M · 2023
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Holistic evaluation of language models
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Leveraging Large Language Models for Multiple Choice Question Answering
Robinson, J. and Wingate, D · 2022
Cited alongside, same era.
Sos-based nonlinear observer design for simultaneous state and disturbance estimation designed for a pmsm model
Sel, A., Sel, B., Coskun, U., and Kasnakoglu, C · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al · 2022
Cited alongside, same era.
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them, October 2022
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
Cited alongside, same era.
Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al · 2022
Cited alongside, same era.
Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., et al · 2023
Closest in time.
Summary of chatgpt/gpt-4 research and perspective towards the future of large language models
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al · 2023
Closest in time.
Large language model guided tree-of-thought
Long, J · 2023
Closest in time.
Faithful chain-of-thought reasoning
Lyu, Q., Havaldar, S., Stein, A., Zhang, L., Rao, D., Wong, E., Apidianaki, M., and Callison-Burch, C · 2023
Closest in time.
The expresssive power of transformers with chain of thought
Merrill, W. and Sabharwal, A · 2023
Closest in time.
Augmented language models: a survey
Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al · 2023
Closest in time.
Memory augmented large language models are computationally universal
Schuurmans, D · 2023
Closest in time.
Learning-to-learn to guide random search: Derivative-free meta blackbox optimization on manifold
Sel, B., Tawaha, A., Ding, Y., Jia, R., Ji, B., Lavaei, J., and Jin, M · 2023
Closest in time.
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., and Chen, W · 2023
Closest in time.
Turpin, M., Michael, J., Perez, E., and Bowman, S. R · 2023
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models, May 2023
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
Closest in time.
Skin-in-the-game: Decision making via multi-stakeholder alignment in llms
Sel, B., Shanmugasundaram, P., Kachuee, M., Zhou, K., Jia, R., and Jin, M · 2024
Closest in time.