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Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathematical computations.
Direct and indirect effects
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Causality
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Learning to solve arithmetic word problems with verb categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., and Kushman, N · 2014
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Learning to automatically solve algebra word problems
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Parsing algebraic word problems into equations
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How well do computers solve math word problems? large-scale dataset construction and evaluation
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Deep neural solver for math word problems
Wang, Y., Liu, X., and Shi, S · 2017
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Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph
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BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Analysing mathematical reasoning abilities of neural models
Saxton, D., Grefenstette, E., Hill, F., and Kohli, P · 2019
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Attention is not not explanation
Wiegreffe, S. and Pinter, Y · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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Compositional explanations of neurons
Mu, J. and Andreas, J · 2020
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Investigating gender bias in language models using causal mediation analysis
Vig, J., Gehrmann, S., Belinkov, Y., Qian, S., Nevo, D., Singer, Y., and Shieber, S · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
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An interpretability illusion for BERT
Bolukbasi, T., Pearce, A., Yuan, A., Coenen, A., Reif, E., Viégas, F. B., and Wattenberg, M · 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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A mathematical framework for transformer circuits
Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., Amodei, D., Brown, T., Clark, J., Kaplan, J., McCandlish, S., and Olah, C · 2021
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Causal analysis of syntactic agreement mechanisms in neural language models
Finlayson, M., Mueller, A., Gehrmann, S., Shieber, S. M., Linzen, T., and Belinkov, Y · 2021
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Causal abstractions of neural networks
Geiger, A., Lu, H., Icard, T., and Potts, C · 2021
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Natural language descriptions of deep visual features
Hernandez, E., Schwettmann, S., Bau, D., Bagashvili, T., Torralba, A., and Andreas, J · 2021
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STar: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
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Mathematical capabilities of chatgpt
Frieder, S., Pinchetti, L., Griffiths, R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., Chevalier, A., and Berner, J · 2023
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Dissecting recall of factual associations in auto-regressive language models
Geva, M., Bastings, J., Filippova, K., and Globerson, A · 2023
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Localizing model behavior with path patching
Goldowsky-Dill, N., MacLeod, C., Sato, L., and Arora, A · 2023
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Investigating the limitations of the transformers with simple arithmetic tasks
Nogueira, R. F., Jiang, Z., and Lin, 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
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Representing numbers in NLP: a survey and a vision
Thawani, A., Pujara, J., Ilievski, F., and Szekely, P. A · 2021
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Flamingo: a visual language model for few-shot learning
Alayrac, J., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J. L., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., and Simonyan, K · 2022
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Hidden progress in deep learning: Sgd learns parities near the computational limit
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Palm: Scaling language modeling with pathways
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Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Geva, M., Caciularu, A., Wang, K. R., and Goldberg, Y · 2022
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Hanna, M., Liu, O., and Variengien, A · 2023
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Mathprompter: Mathematical reasoning using large language models
Imani, S., Du, L., and Shrivastava, H · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., et al · 2023
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Making language models better reasoners with step-aware verifier
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W · 2023
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Lieberum, T., Rahtz, M., Kramár, J., Nanda, N., Irving, G., Shah, R., and Mikulik, V · 2023
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Learning math reasoning from self-sampled correct and partially-correct solutions
Ni, A., Inala, J. P., Wang, C., Polozov, A., Meek, C., Radev, D., and Gao, J · 2023
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Zoom in: An introduction to circuits
Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., and Carter, S · 2023
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Task-specific skill localization in fine-tuned language models
Panigrahi, A., Saunshi, N., Zhao, H., and Arora, S · 2023
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Limitations of language models in arithmetic and symbolic induction
Qian, J., Wang, H., Li, Z., Li, S., and Yan, X · 2023
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Toward transparent ai: A survey on interpreting the inner structures of deep neural networks
Rauker, T., Ho, A., Casper, S., and Hadfield-Menell, D · 2023
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Understanding arithmetic reasoning in language models using causal mediation analysis
Stolfo, A., Belinkov, Y., and Sachan, M · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto., T. B · 2023
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Interpretability at scale: Identifying causal mechanisms in alpaca
Wu, Z., Geiger, A., Potts, C., and Goodman, N. D · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Yu, L., Bowen, Y., Yu, H., Huang, F., and Li, Y · 2023
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Do language models exhibit the same cognitive biases in problem solving as human learners?
Opedal, A., Stolfo, A., Shirakami, H., Jiao, Y., Cotterell, R., Schölkopf, B., Saparov, A., and Sachan, M · 2024
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., Kohli, P., and Fawzi, A · 2024
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