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Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2019 · 1910
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ROUGE: A Package for Automatic Evaluation of Summaries
Lin, C.-Y. 2004 · 2004
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The turking test: Can language models understand instructions?
Efrat, A.; and Levy, O. 2020 · 2010
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
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Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Keysers, D.; Schärli, N.; Scales, N.; Buisman, H.; Furrer, D.; Kashubin, S.; Momchev, N.; Sinopalnikov, D.; Stafiniak, L.; Tihon, T.; et al. 2019 · 2019
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X.; Duong Nguyen, A.-K.; Sugawara, S.; and Aizawa, A. 2020 · 2020
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COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
Kim, N.; and Linzen, T. 2020 · 2020
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WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
Ladhak, F.; Durmus, E.; Cardie, C.; and McKeown, K. 2020 · 2020
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Learning from Task Descriptions
Weller, O.; Lourie, N.; Gardner, M.; and Peters, M. E. 2020 · 2020
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Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V.; Webson, A.; Raffel, C.; Bach, S.; Sutawika, L.; Alyafeai, Z.; Chaffin, A.; Stiegler, A.; Raja, A.; Dey, M.; et al. 2021 · 2021
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Generating Datasets with Pretrained Language Models
Schick, T.; and Schütze, H. 2021 · 2021
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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 · 2022
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Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Mishra, S.; Khashabi, D.; Baral, C.; and Hajishirzi, H. 2022 · 2022
Cited alongside, same era.
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. 2022 · 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 · 2022
Cited alongside, same era.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Qiu, L.; Shaw, P.; Pasupat, P.; Nowak, P.; Linzen, T.; Sha, F.; and Toutanova, K. 2022 · 2022
Cited alongside, same era.
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
Saparov, A.; and He, H. 2022 · 2022
Cited alongside, same era.
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Honovich, O.; Scialom, T.; Levy, O.; and Schick, T. 2023 · 2023
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Hsieh, C.-Y.; Li, C.-L.; Yeh, C.-k.; Nakhost, H.; Fujii, Y.; Ratner, A.; Krishna, R.; Lee, C.-Y.; and Pfister, T. 2023 · 2023
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Jiang, A. Q.; Sablayrolles, A.; Mensch, A.; Bamford, C.; Chaplot, D. S.; Casas, D. d. l.; Bressand, F.; Lengyel, G.; Lample, G.; Saulnier, L.; et al. 2023 · 2023
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G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment
Liu, Y.; Iter, D.; Xu, Y.; Wang, S.; Xu, R.; and Zhu, C. 2023 · 2023
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Peng, B.; Li, C.; He, P.; Galley, M.; and Gao, J. 2023 · 2023
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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 · 2022
Cited alongside, same era.
MuSiQue: Multihop Questions via Single-hop Question Composition
Trivedi, H.; Balasubramanian, N.; Khot, T.; and Sabharwal, A. 2022 · 2022
Cited alongside, same era.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Wang, Y.; Mishra, S.; Alipoormolabashi, P.; Kordi, Y.; Mirzaei, A.; Naik, A.; Ashok, A.; Dhanasekaran, A. S.; Arunkumar, A.; Stap, D.; Pathak, E.; Karamanolakis, G.; Lai, H.; Purohit, I.; Mondal, I.; Anderson, J.; Kuznia, K.; Doshi, K.; Pal, K. K.; Patel, M.; Moradshahi, M.; Parmar, M.; Purohit, M.; Varshney, N.; Kaza, P. R.; Verma, P.; Puri, R. S.; Karia, R.; Doshi, S.; Sampat, S. K.; Mishra, S.; Reddy A, S.; Patro, S.; Dixit, T.; and Shen, X. 2022 · 2022
Cited alongside, same era.
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
West, P.; Bhagavatula, C.; Hessel, J.; Hwang, J.; Jiang, L.; Le Bras, R.; Lu, X.; Welleck, S.; and Choi, Y. 2022 · 2022
Cited alongside, same era.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Cui, C.; Bousquet, O.; Le, Q. V.; et al. 2022 · 2022
Cited alongside, same era.
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Aksu, T.; Hazarika, D.; Mehri, S.; Kim, S.; Hakkani-Tür, D.; Liu, Y.; and Namazifar, M. 2023 · 2023
Cited alongside, same era.
Free Dolly: Introducing the World’s First Truly Open Instruction-Tuned LLM
Conover, M.; Hayes, M.; Mathur, A.; Xie, J.; Wan, J.; Shah, S.; Ghodsi, A.; Wendell, P.; Zaharia, M.; and Xin, R. 2023 · 2023
Cited alongside, same era.
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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 · 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 · 2023
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Self-Instruct: Aligning Language Models with Self-Generated Instructions
Wang, Y.; Kordi, Y.; Mishra, S.; Liu, A.; Smith, N. A.; Khashabi, D.; and Hajishirzi, H. 2023 · 2023
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Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Yin, F.; Vig, J.; Laban, P.; Joty, S.; Xiong, C.; and Wu, C.-S. 2023 · 2023
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Scaling Instruction-Finetuned Language Models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, Y.; Wang, X.; Dehghani, M.; Brahma, S.; Webson, A.; Gu, S. S.; Dai, Z.; Suzgun, M.; Chen, X.; Chowdhery, A.; Castro-Ros, A.; Pellat, M.; Robinson, K.; Valter, D.; Narang, S.; Mishra, G.; Yu, A.; Zhao, V.; Huang, Y.; Dai, A.; Yu, H.; Petrov, S.; Chi, E. H.; Dean, J.; Devlin, J.; Roberts, A.; Zhou, D.; Le, Q. V.; and Wei, J. 2024 · 2024
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Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Das, D.; De Langis, K.; Martin, A.; Kim, J.; Lee, M.; Kim, Z. M.; Hayati, S. A.; Owan, R.; Hu, B.; Parkar, R.; et al. 2024 · 2024
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GPTScore: Evaluate as You Desire
Fu, J.; Ng, S.-K.; Jiang, Z.; and Liu, P. 2024 · 2024
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MiniLLM: Knowledge Distillation of Large Language Models
Gu, Y.; Dong, L.; Wei, F.; and Huang, M. 2024 · 2024
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