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Instruction-following is particularly crucial for large language models (LLMs) to support diverse user requests.
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dua, D.; Wang, Y.; Dasigi, P.; Stanovsky, G.; Singh, S.; and Gardner, M. 2019 · 1903
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2019 · 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 · 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 · 2020
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LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Liu, J.; Cui, L.; Liu, H.; Huang, D.; Wang, Y.; and Zhang, Y. 2020 · 2020
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Program Synthesis with Large Language Models
Austin, J.; Odena, A.; Nye, M. I.; Bosma, M.; Michalewski, H.; Dohan, D.; Jiang, E.; Cai, C. J.; Terry, M.; Le, Q. V.; and Sutton, C. 2021 · 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 · 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 · 2021
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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 · 2021
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Reframing human-AI collaboration for generating free-text explanations
Wiegreffe, S.; Hessel, J.; Swayamdipta, S.; Riedl, M.; and Choi, Y. 2021 · 2021
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Emergent Abilities of Large Language Models
Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; Chi, E. H.; Hashimoto, T.; Vinyals, O.; Liang, P.; Dean, J.; and Fedus, W. 2022 · 2022
Cited alongside, same era.
ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
Azerbayev, Z.; Piotrowski, B.; Schoelkopf, H.; Ayers, E. W.; Radev, D.; and Avigad, J. 2023 · 2023
Cited alongside, same era.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Alpacafarm: A simulation framework for methods that learn from human feedback
Dubois, Y.; Li, X.; Taori, R.; Zhang, T.; Gulrajani, I.; Ba, J.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023 · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Longpre, S.; Hou, L.; Vu, T.; Webson, A.; Chung, H. W.; Tay, Y.; Zhou, D.; Le, Q. V.; Zoph, B.; Wei, J.; et al. 2023 · 2023
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OpenAI. 2023 · 2023
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How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Wang, Y.; Ivison, H.; Dasigi, P.; Hessel, J.; Khot, T.; Chandu, K. R.; Wadden, D.; MacMillan, K.; Smith, N. A.; Beltagy, I.; and Hajishirzi, H. 2023 · 2023
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bench authors, B. 2023 · 2023
Cited alongside, same era.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y. T.; Li, Y.; Lundberg, S. M.; Nori, H.; Palangi, H.; Ribeiro, M. T.; and Zhang, Y. 2023 · 2023
Cited alongside, same era.
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Chia, Y. K.; Hong, P.; Bing, L.; and Poria, S. 2023 · 2023
Cited alongside, same era.
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; and Xing, E. P. 2023 · 2023
Cited alongside, same era.
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. 2021a
Cited in the paper.
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. 2021b
Cited in the paper.
Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023a
Cited in the paper.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023b
Cited in the paper.
Ye, S.; Kim, D.; Kim, S.; Hwang, H.; Kim, S.; Jo, Y.; Thorne, J.; Kim, J.; and Seo, M. 2023 · 2023
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Distilling Script Knowledge from Large Language Models for Constrained Language Planning
Yuan, S.; Chen, J.; Fu, Z.; Ge, X.; Shah, S.; Jankowski, C. R.; Yang, D.; and Xiao, Y. 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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Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Zheng, L.; Chiang, W.-L.; Sheng, Y.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Lin, Z.; Li, Z.; Li, D.; Xing, E.; et al. 2023 · 2023
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