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Pre-trained language models (LMs) are able to perform complex reasoning without explicit fine-tuning.
A new model for earning in raph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
Earlier work this paper cites.
Markov logic networks
Richardson, M. and Domingos, P · 2006
Earlier work this paper cites.
Statistical predicate invention
Kok, S. and Domingos, P · 2007
Earlier work this paper cites.
Random walk inference and learning in a large scale knowledge base
Lao, N., Mitchell, T., and Cohen, W. W · 2011
Earlier work this paper cites.
On approximate reasoning capabilities of low-rank vector spaces
Bouchard, G., Singh, S., and Trouillon, T · 2015
Earlier work this paper cites.
Representing text for joint embedding of text and knowledge bases
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., and Gamon, M · 2015
Earlier work this paper cites.
MAWPS: A math word problem repository
Koncel-Kedziorski, R., Roy, S., Amini, A., Kushman, N., and Hajishirzi, H · 2016
Earlier work this paper cites.
Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A., and McCallum, A · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Earlier work this paper cites.
Deeppath: A reinforcement learning method for knowledge graph reasoning
Xiong, W., Hoang, T., and Wang, W. Y · 2017
Earlier work this paper cites.
Differentiable learning of logical rules for knowledge base reasoning
Yang, F., Yang, Z., and Cohen, W. W · 2017
Earlier work this paper cites.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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.
Artificial intelligence / machine learning research using the australian aboriginal alyawarra kinship dataset: Partial bibliography 2004-2020
Denham, W · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Earlier work this paper cites.
A diverse corpus for evaluating and developing English math word problem solvers
Miao, S.-y., Liang, C.-C., and Su, K.-Y · 2020
Earlier work this paper cites.
Rnnlogic: Learning logic rules for reasoning on knowledge graphs
Qu, M., Chen, J., Xhonneux, L.-P., Bengio, Y., and Tang, J · 2020
Earlier work this paper cites.
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., et al · 2021
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Causal abstractions of neural networks
Geiger, A., Lu, H., Icard, T., and Potts, C · 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
Cited alongside, same era.
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 · 2021
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
Jain, S., Kirk, R., Lubana, E. S., Dick, R. P., Tanaka, H., Grefenstette, E., Rocktäschel, T., and Krueger, D. S · 2023
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Dissecting chain-of-thought: A study on compositional in-context learning of mlps
Li, Y., Sreenivasan, K., Giannou, A., Papailiopoulos, D., and Oymak, S · 2023
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Transformers learn shortcuts to automata
Liu, B., Ash, J. T., Goel, S., Krishnamurthy, A., and Zhang, C · 2023
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Auto-regressive next-token predictors are universal learners
Malach, E · 2023
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Triggering multi-hop reasoning for question answering in language models using soft prompts and random walks
Misra, K., Nogueira dos Santos, C., and Shakeri, S · 2023
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Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2021
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.
Show your work: Scratchpads for intermediate computation with language models, 2022
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., and Odena, A · 2022
Cited alongside, same era.
Impact of pretraining term frequencies on few-shot reasoning
Razeghi, Y., Logan IV, R. L., Gardner, M., and Singh, S · 2022
Cited alongside, same era.
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., et al · 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.
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Logic-LM: Empowering large language models with symbolic solvers for faithful logical reasoning
Pan, L., Albalak, A., Wang, X., and Wang, W · 2023
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Why think step by step? reasoning emerges from the locality of experience
Prystawski, B., Li, M. Y., and Goodman, N · 2023
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How capable can a transformer become? a study on synthetic, interpretable tasks
Ramesh, R., Khona, M., Dick, R. P., Tanaka, H., and Lubana, E. S · 2023
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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., Kluska, A., Lewkowycz, A., Agarwal, A., Power, A., Ray, A., Warstadt, A., Kocurek, A. W., Safaya, A., Tazarv, A., Xiang, A., Parrish, A., Nie, A., Hussain, A., Askell, A., Dsouza, A., Slone, A., Rahane, A., Iyer, A. S., Andreassen, A. J., Madotto, A., Santilli, A., Stuhlmüller, A., Dai, A. M., La, A., Lampinen, A., Zou, A., Jiang, A., Chen, A., Vuong, A., Gupta, A., Gottardi, A., Norelli, A., Venkatesh, A., Gholamidavoodi, A., Tabassum, A., Menezes, A., Kirubarajan, A., Mullokandov, A., Sabharwal, A., Herrick, A., Efrat, A., Erdem, A., Karakaş, A., Roberts, B. R., Loe, B. S., Zoph, B., Bojanowski, B., Özyurt, B., Hedayatnia, B., Neyshabur, B., Inden, B., Stein, B., Ekmekci, B., Lin, B. Y., Howald, B., Orinion, B., Diao, C., Dour, C., Stinson, C., Argueta, C., Ferri, C., Singh, C., Rathkopf, C., Meng, C., Baral, C., Wu, C., Callison-Burch, C., Waites, C., Voigt, C., Manning, C. D., Potts, C., Ramirez, C., Rivera, C. E., Siro, C., Raffel, C., Ashcraft, C., Garbacea, C., Sileo, D., Garrette, D., Hendrycks, D., Kilman, D., Roth, D., Freeman, C. D., Khashabi, D., Levy, D., González, D. M., Perszyk, D., Hernandez, D., Chen, D., Ippolito, D., Gilboa, D., Dohan, D., Drakard, D., Jurgens, D., Datta, D., Ganguli, D., Emelin, D., Kleyko, D., Yuret, D., Chen, D., Tam, D., Hupkes, D., Misra, D., Buzan, D., Mollo, D. C., Yang, D., Lee, D.-H., Schrader, D., Shutova, E., Cubuk, E. D., Segal, E., Hagerman, E., Barnes, E., Donoway, E., Pavlick, E., Rodolà, E., Lam, E., Chu, E., Tang, E., Erdem, E., Chang, E., Chi, E. A., Dyer, E., Jerzak, E., Kim, E., Manyasi, E. E., Zheltonozhskii, E., Xia, F., Siar, F., Martínez-Plumed, F., Happé, F., Chollet, F., Rong, F., Mishra, G., Winata, G. I., de Melo, G., Kruszewski, G., Parascandolo, G., Mariani, G., Wang, G. X., Jaimovitch-Lopez, G., Betz, G., Gur-Ari, G., Galijasevic, H., Kim, H., Rashkin, H., Hajishirzi, H., Mehta, H., Bogar, H., Shevlin, H. F. A., Schuetze, H., Yakura, H., Zhang, H., Wong, H. M., Ng, I., Noble, I., Jumelet, J., Geissinger, J., Kernion, J., Hilton, J., Lee, J., Fisac, J. F., Simon, J. B., Koppel, J., Zheng, J., Zou, J., Kocon, J., Thompson, J., Wingfield, J., Kaplan, J., Radom, J., Sohl-Dickstein, J., Phang, J., Wei, J., Yosinski, J., Novikova, J., Bosscher, J., Marsh, J., Kim, J., Taal, J., Engel, J., Alabi, J., Xu, J., Song, J., Tang, J., Waweru, J., Burden, J., Miller, J., Balis, J. U., Batchelder, J., Berant, J., Frohberg, J., Rozen, J., Hernandez-Orallo, J., Boudeman, J., Guerr, J., Jones, J., Tenenbaum, J. B., Rule, J. S., Chua, J., Kanclerz, K., Livescu, K., Krauth, K., Gopalakrishnan, K., Ignatyeva, K., Markert, K., Dhole, K., Gimpel, K., Omondi, K., Mathewson, K. W., Chiafullo, K., Shkaruta, K., Shridhar, K., McDonell, K., Richardson, K., Reynolds, L., Gao, L., Zhang, L., Dugan, L., Qin, L., Contreras-Ochando, L., Morency, L.-P., Moschella, L., Lam, L., Noble, L., Schmidt, L., He, L., Oliveros-Colón, L., Metz, L., Senel, L. K., Bosma, M., Sap, M., Hoeve, M. T., Farooqi, M., Faruqui, M., Mazeika, M., Baturan, M., Marelli, M., Maru, M., Ramirez-Quintana, M. J., Tolkiehn, M., Giulianelli, M., Lewis, M., Potthast, M., Leavitt, M. L., Hagen, M., Schubert, M., Baitemirova, M. O., Arnaud, M., McElrath, M., Yee, M. A., Cohen, M., Gu, M., Ivanitskiy, M., Starritt, M., Strube, M., Swedrowski, M., Bevilacqua, M., Yasunaga, M., Kale, M., Cain, M., Xu, M., Suzgun, M., Walker, M., Tiwari, M., Bansal, M., Aminnaseri, M., Geva, M., Gheini, M., T, M. V., Peng, N., Chi, N. A., Lee, N., Krakover, N. G.-A., Cameron, N., Roberts, N., Doiron, N., Martinez, N., Nangia, N., Deckers, N., Muennighoff, N., Keskar, N. S., Iyer, N. S., Constant, N., Fiedel, N., Wen, N., Zhang, O., Agha, O., Elbaghdadi, O., Levy, O., Evans, O., Casares, P. A. M., Doshi, P., Fung, P., Liang, P. P., Vicol, P., Alipoormolabashi, P., Liao, P., Liang, P., Chang, P. W., Eckersley, P., Htut, P. M., Hwang, P., Miłkowski, P., Patil, P., Pezeshkpour, P., Oli, P., Mei, Q., Lyu, Q., Chen, Q., Banjade, R., Rudolph, R. E., Gabriel, R., Habacker, R., Risco, R., Millière, R., Garg, R., Barnes, R., Saurous, R. A., Arakawa, R., Raymaekers, R., Frank, R., Sikand, R., Novak, R., Sitelew, R., Bras, R. L., Liu, R., Jacobs, R., Zhang, R., Salakhutdinov, R., Chi, R. A., Lee, S. R., Stovall, R., Teehan, R., Yang, R., Singh, S., Mohammad, S. M., Anand, S., Dillavou, S., Shleifer, S., Wiseman, S., Gruetter, S., Bowman, S. R., Schoenholz, S. S., Han, S., Kwatra, S., Rous, S. A., Ghazarian, S., Ghosh, S., Casey, S., Bischoff, S., Gehrmann, S., Schuster, S., Sadeghi, S., Hamdan, S., Zhou, S., Srivastava, S., Shi, S., Singh, S., Asaadi, S., Gu, S. S., Pachchigar, S., Toshniwal, S., Upadhyay, S., Debnath, S. S., Shakeri, S., Thormeyer, S., Melzi, S., Reddy, S., Makini, S. P., Lee, S.-H., Torene, S., Hatwar, S., Dehaene, S., Divic, S., Ermon, S., Biderman, S., Lin, S., Prasad, S., Piantadosi, S., Shieber, S., Misherghi, S., Kiritchenko, S., Mishra, S., Linzen, T., Schuster, T., Li, T., Yu, T., Ali, T., Hashimoto, T., Wu, T.-L., Desbordes, T., Rothschild, T., Phan, T., Wang, T., Nkinyili, T., Schick, T., Kornev, T., Tunduny, T., Gerstenberg, T., Chang, T., Neeraj, T., Khot, T., Shultz, T., Shaham, U., Misra, V., Demberg, V., Nyamai, V., Raunak, V., Ramasesh, V. V., vinay uday prabhu, Padmakumar, V., Srikumar, V., Fedus, W., Saunders, W., Zhang, W., Vossen, W., Ren, X., Tong, X., Zhao, X., Wu, X., Shen, X., Yaghoobzadeh, Y., Lakretz, Y., Song, Y., Bahri, Y., Choi, Y., Yang, Y., Hao, Y., Chen, Y., Belinkov, Y., Hou, Y., Hou, Y., Bai, Y., Seid, Z., Zhao, Z., Wang, Z., Wang, Z. J., Wang, Z., and Wu, Z · 2023
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Towards understanding chain-of-thought prompting: An empirical study of what matters
Wang, B., Min, S., Deng, X., Shen, J., Wu, Y., Zettlemoyer, L., and Sun, H · 2023
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Interpretability at scale: Identifying causal mechanisms in alpaca
Wu, Z., Geiger, A., Icard, T., Potts, C., and Goodman, N · 2023
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Conditions for length generalization in learning reasoning skills
Xiao, C. and Liu, B · 2023
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Leandojo: Theorem proving with retrieval-augmented language models
Yang, K., Swope, A. M., Gu, A., Chalamala, R., Song, P., Yu, S., Godil, S., Prenger, R., and Anandkumar, A · 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
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What algorithms can transformers learn? a study in length generalization
Zhou, H., Bradley, A., Littwin, E., Razin, N., Saremi, O., Susskind, J., Bengio, S., and Nakkiran, P · 2023
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Gemma: Open models based on gemini research and technology
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