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Chain-of-thought prompting (CoT) has the potential to improve the explainability of language model reasoning.
On the Measure of Intelligence
François Chollet · 1911
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Towards A Rigorous Science of Interpretable Machine Learning, March 2017
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Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
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Unsupervised Question Decomposition for Question Answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela · 2020
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Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
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Measuring and Improving Consistency in Pretrained Language Models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg · 2021
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Measuring Massive Multitask Language Understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Measuring Progress on Scalable Oversight for Large Language Models, November 2022
Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamilė Lukošiūtė, Amanda Askell, Andy Jones, Anna Chen, Anna Goldie, et al · 2022
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Faithful Reasoning Using Large Language Models, August 2022
Antonia Creswell and Murray Shanahan · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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TruthfulQA: Measuring How Models Mimic Human Falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2022
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Show your work: Scratchpads for intermediate computation with language models, 2022
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, et al · 2022
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Discovering Language Model Behaviors with Model-Written Evaluations, December 2022
Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al · 2022
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Alpaca dataset from Stanford, cleaned and curated, 2023
Gene Ruebsamen · 2023
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Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, et al · 2023
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Large Language Models Can Be Easily Distracted by Irrelevant Context, February 2023
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou · 2023
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Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
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Stanford Alpaca: An Instruction-following LLaMA model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Solving math word problems with process- and outcome-based feedback, November 2022
Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins · 2022
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Chain of Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Prompt Consistency for Zero-Shot Task Generalization
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Faithfulness tests for natural language explanations
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Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, and Kathleen McKeown · 2023
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The Capacity for Moral Self-Correction in Large Language Models, February 2023
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Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
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Representation Engineering: A Top-Down Approach to AI Transparency, October 2023
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Quantifying Language Models’ Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
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Simple synthetic data reduces sycophancy in large language models, February 2024
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DeCoT: Debiasing chain-of-thought for knowledge-intensive tasks in large language models via causal intervention
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Causal prompting: Debiasing large language model prompting based on front-door adjustment
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