Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks.
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.
e-snli: Natural language inference with natural language explanations
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., and Blunsom, P · 2018
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Rajani, N. F., McCann, B., Xiong, C., and Socher, R · 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.
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.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
Earlier work this paper cites.
Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge
Talmor, A., Tafjord, O., Clark, P., Goldberg, Y., and Berant, J · 2020
Earlier work this paper cites.
Explanations for commonsenseqa: New dataset and models
Aggarwal, S., Mandowara, D., Agrawal, V., Khandelwal, D., Singla, P., and Garg, D · 2021
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. v. d., Lespiau, J.-B., Damoc, B., Clark, A., et al · 2021
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Show your work: Scratchpads for intermediate computation with language models
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., et al · 2021
Earlier work this paper cites.
Creak: A dataset for commonsense reasoning over entity knowledge
Onoe, Y., Zhang, M. J., Choi, E., and Durrett, G · 2021
Earlier work this paper cites.
Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al · 2021
Earlier work this paper cites.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al · 2021
Cited alongside, same era.
Towards zero-label language learning
Wang, Z., Yu, A. W., Firat, O., and Cao, Y · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Cited alongside, same era.
Promptsource: An integrated development environment and repository for natural language prompts
Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al · 2022
Cited alongside, same era.
The bigscience roots corpus: A 1.6 tb composite multilingual dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Le Scao, T., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al · 2022
Later among the works it cites.
Crosslingual generalization through multitask finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Scao, T. L., Bari, M. S., Shen, S., Yong, Z.-X., Schoelkopf, H., et al · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
Cited alongside, same era.
Large language models are reasoning teachers
Ho, N., Schmid, L., and Yun, S.-Y · 2022
Cited alongside, same era.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Cited alongside, same era.
Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Iyer, S., Lin, X. V., Pasunuru, R., Mihaylov, T., Simig, D., Yu, P., Shuster, K., Wang, T., Liu, Q., Koura, P. S., et al · 2022
Cited alongside, same era.
Few-shot learning with retrieval augmented language models
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., and Grave, E · 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.
Scialom, T., Chakrabarty, T., and Muresan, S · 2022
Later among the works it cites.
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
Later among the works it cites.
Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
Later among the works it cites.
Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., and Goodman, N. D · 2022
Later among the works it cites.
Glm-130b: An open bilingual pre-trained model
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., et al · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
Later among the works it cites.
Prompt2model: Generating deployable models from natural language instructions
Viswanathan, V., Zhao, C., Bertsch, A., Wu, T., and Neubig, G · 2023
Closest in time.
Internet of agents: Weaving a web of heterogeneous agents for collaborative intelligence
Chen, W., You, Z., Li, R., Guan, Y., Qian, C., Zhao, C., Yang, C., Xie, R., Liu, Z., and Sun, M · 2024
Closest in time.
Minicpm: Unveiling the potential of small language models with scalable training strategies
Hu, S., Tu, Y., Han, X., He, C., Cui, G., Long, X., Zheng, Z., Fang, Y., Huang, Y., Zhao, W., et al · 2024
Closest in time.