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home / papers / 2023 / DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Paper · 2023 · Jing Xiong, Zixuan Li, Chuanyang Zheng et al.

Summary

The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems. Existing retrieval-based selectors mostly match exemplars by how similar the question text is. The authors argue that this ignores the reasoning itself. They also argue that high-dimensional sentence embeddings carry redundant, anisotropic information, which makes candidate exemplars hard to tell apart.

Their framework, DQ-LoRe, works with two queries:

  • First query: the LLM is prompted, starting from Complex-CoT exemplars, to write a chain-of-thought for the test question.
  • Second query: the question and this generated CoT together are used to search a retriever over the training pool.

The retrieved candidates are then re-ranked after PCA projects their embeddings into a lower-rank space. The retriever is trained contrastively, with positive and negative sets built from CoT similarity using BM25 and LLM feedback.

With GPT-4, DQ-LoRe lifts accuracy from 92.5%, the prior best automatic selection result, to 94.2%. It is particularly robust under distribution shift. This suggests that reasoning-aware, low-rank retrieval can find better prompts without hand-crafted exemplars.

Key points

  • Pipeline: the LLM generates a CoT, then question plus CoT query the retriever, then PCA-based low-rank re-ranking (LoRe) picks the final exemplars.
  • Complex-CoT supplies the initial n-shot prompt. BM25 and LLM signals build the positive/negative training sets for the retriever.
  • On GPT-4, accuracy improves from 92.5% to 94.2% compared with earlier state-of-the-art exemplar selection. Gains are strongest in distribution-shift settings.
  • A case study contrasts the methods:
    • EPR picks exemplars with matching surface words (all "Mary baking a cake").
    • CEIL favours diverse but unrelated problems.
    • DQ-LoRe finds exemplars with the same reasoning pattern (subtraction then comparison), even when the context differs.
  • Code is released at github.com/menik1126/DQ-LoRe (arXiv:2310.02954).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hong Kong University of Science and TechnologyHong Kong · China142 works · 25,136 citations🎓Chinese University of Hong KongHong Kong · China126 works · 14,073 citations🏢Huawei TechnologiesShenzhen · China75 works · 12,975 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates124 works · 5,676 citations🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China53 works · 1,367 citations🎓Shenzhen MSU-BIT UniversityShenzhen · China3 works · 38 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 worksInformation retrieval & search · 473 worksEfficient models & compression · 242 works

Cite

Cite as Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li and Xiaodan Liang. 2023. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning. arXiv:2310.02954.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2310.02954 · arXiv 2310.02954

@misc{xiong2023dq,
  title = {{DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning}},
  author = {Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li and Xiaodan Liang},
  year = {2023},
  doi = {10.48550/arxiv.2310.02954},
  eprint = {2310.02954},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.02954}
}

References

73 references, 36 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Se… — PaLM 2 Technical Report
  2. Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023. find
  3. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  4. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.
  5. Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna.lmsys. org (accessed 14 April 2023), 2023. ↗
  6. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emi… — PaLM: Scaling Language Modeling with Pathways
  7. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. https://www.mindspore.cn/ ↗
  8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  9. Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. Complexity-based prompting for multi-step reasoning. arXiv preprint arXiv:2210.00720, 2022.
  10. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. PAL: program-aided language models. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), International Conference on Machine Learning,… find
  11. PMLR, 2023. URL https://proceedings.mlr.press/v202/gao23f.html. ↗
  12. Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346–361, 2021. find
  13. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  14. Junjie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, and Nan Duan. Whiteningbert: An easy unsupervised sentence embedding approach. arXiv preprint arXiv:2104.01767, 2021. find
  15. Yinya Huang, Xiaohan Lin, Zhengying Liu, Qingxing Cao, Huajian Xin, Haiming Wang, Zhenguo Li, Linqi Song, and Xiaodan Liang. Mustard: Mastering uniform synthesis of theorem and proof data. arXiv preprint arXiv:2402.08957, 2024. find
  16. Aapo Hyvarinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE transactions on Neural Networks, 10(3):626–634, 1999. find
  17. Aapo Hyvärinen and Erkki Oja. Independent component analysis: algorithms and applications. Neural networks, 13(4-5):411–430, 2000. find
  18. Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906, 2020.
  19. Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. Qasc: A dataset for question answering via sentence composition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp. 8082–8090, 2020. find
  20. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  21. Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3:585–597, 2015. find
  22. Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra. Solving quantitative reasoning problems with language models. In NeurIPS, 2022. UR…
  23. Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. On the sentence embeddings from pre-trained language models. arXiv preprint arXiv:2011.05864, 2020.
  24. Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, and Xipeng Qiu. Unified demonstration retriever for in-context learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4644–4668, Toront…
  25. Zhongli Li, Wenxuan Zhang, Chao Yan, Qingyu Zhou, Chao Li, Hongzhi Liu, and Yunbo Cao. Seeking patterns, not just memorizing procedures: Contrastive learning for solving math word problems. arXiv preprint arXiv:2110.08464, 2021. find
  26. Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. Program induction by rationale generation: Learning to solve and explain algebraic word problems. arXiv preprint arXiv:1705.04146, 2017. find
  27. Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. What makes good in-context examples for gpt-3? In Eneko Agirre, Marianna Apidianaki, and Ivan Vulic (eds.), Proceedings of Deep Learning Inside Out: The 3rd Workshop on Knowledge Extraction and Integration for Deep…
  28. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput. Surv., 55(9):195:1–195:35, 2023. doi: 10.1145/3560815. URL https://doi.org/10.1145/3560815. ↗
  29. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
  30. Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Fei Mi, Baojun Wang, Weichao Wang, Lifeng Shang, and Qun Liu. Self: Language-driven self-evolution for large language model. arXiv preprint arXiv:2310.00533, 2023. — [arXiv:2310.00533](https://arxiv.org/abs/2310.00533) find
  31. Jianqiao Lu, Wanjun Zhong, Yufei Wang, Zhijiang Guo, Qi Zhu, Wenyong Huang, Yanlin Wang, Fei Mi, Baojun Wang, Yasheng Wang, et al. Yoda: Teacher-student progressive learning for language models. arXiv preprint arXiv:2401.15670, 2024. — [arXiv:2401.15670](https://arxiv.org/abs/2401.15670) find
  32. Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (eds.), Proceedings of the 60th Annual Meeting of the Associati…
  33. OpenAI. GPT-4 technical report. CoRR, abs/2303.08774, 2023. doi: 10.48550/arXiv.2303.08774. URL https://doi.org/10.48550/arXiv.2303.08774. ↗
  34. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. Train… — Training language models to follow instructions with human feedback
  35. Yu Pan, Zeyong Su, Ao Liu, Jingquan Wang, Nannan Li, and Zenglin Xu. A unified weight initialization paradigm for tensorial convolutional neural networks. In ICML, volume 162 of Proceedings of Machine Learning Research, pp. 17238–17257. PMLR, 2022. find
  36. Arkil Patel, Satwik Bhattamishra, and Navin Goyal. Are nlp models really able to solve simple math word problems? arXiv preprint arXiv:2103.07191, 2021.
  37. Stephen Robertson, Hugo Zaragoza, et al. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333–389, 2009. find
  38. Subhro Roy and Dan Roth. Solving general arithmetic word problems. In Lluís Màrquez, Chris Callison-Burch, Jian Su, Daniele Pighin, and Yuval Marton (eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, pp…
  39. Ohad Rubin, Jonathan Herzig, and Jonathan Berant. Learning to retrieve prompts for in-context learning. arXiv preprint arXiv:2112.08633, 2021.
  40. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonatha… — Multitask Prompted Training Enables Zero-Shot Task Generalization
  41. OpenReview.net, 2022. URL https://openreview.net/forum?id=9Vrb9D0WI4. ↗
  42. Bernhard Scholkopf, Kah-Kay Sung, Christopher JC Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, and Vladimir Vapnik. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE transactions on Signal Processing, 45(11):2758–2765, 1997. find
  43. Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan, Ming Zhang, and Chenguang Wang. Measuring vision-language stem skills of neural models. arXiv preprint arXiv:2402.17205, 2024. — [arXiv:2402.17205](https://arxiv.org/abs/2402.17205) find
  44. Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.), Proceedings of the 2020 Conference on Empirical Methods in Natura…
  45. Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. Selective annotation makes language models better few-shot learners. In The Eleventh International Conference on Learning Representations, ICLR 2023, Ki… find
  46. OpenReview.net, 2023a. URL https://openreview.net/pdf?id=qY1hlv7gwg. ↗
  47. Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyiwen Ou. Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316, 2021. find
  48. Ying Su, Xiaojin Fu, Mingwen Liu, and Zhijiang Guo. Are llms rigorous logical reasoner? empowering natural language proof generation with contrastive stepwise decoding. arXiv preprint arXiv:2311.06736, 2023b. — [arXiv:2311.06736](https://arxiv.org/abs/2311.06736) find
  49. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. CoRR, abs/2302…
  50. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… — Llama 2: Open Foundation and Fine-Tuned Chat Models
  51. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023c.
  52. Laurens Van der Maaten and Geoffrey Hinton. Visualizing non-metric similarities in multiple maps. Machine learning, 87:33–55, 2012. find
  53. Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. arXiv preprint arXiv:2305.04091, 2023a. find
  54. Maolin Wang, Yu Pan, Xiangli Yang, Guangxi Li, Zenglin Xu, and Andrzej Cichocki. Tensor networks meet neural networks: A survey and future perspectives. CoRR, abs/2302.09019, 2023b. — [arXiv:2302.09019](https://arxiv.org/abs/2302.09019) find
  55. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022.
  56. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022a.
  57. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In NeurIPS, 2022b. URL http://papers.nips.cc/paper_files/paper/2022/hash/ 9d5609613524ecf4f15af0f7b31abca4-Abst… ↗
  58. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022c.
  59. Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3):37–52, 1987. find
  60. Jing Xiong, Chengming Li, Min Yang, Xiping Hu, and Bin Hu. Expression syntax information bottleneck for math word problems. In Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (eds.), SIGIR ’22: The 45th International ACM SIGIR Conference on Resea…
  61. ACM, 2022. doi: 10.1145/3477495.3531824. URL https://doi.org/10.1145/3477495.3531824. — [doi:10.1145/3477495.3531824](https://doi.org/10.1145/3477495.3531824) ↗
  62. Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-seng Chua. Search-in-the-chain: Towards the accurate, credible and traceable content generation for complex knowledge-intensive tasks. arXiv preprint arXiv:2304.14732, 2023. — [arXiv:2304.14732](https://arxiv.org/abs/2304.14732) find
  63. Zhicheng Yang, Jinghui Qin, Jiaqi Chen, and Xiaodan Liang. Unbiased math word problems benchmark for mitigating solving bias. arXiv preprint arXiv:2205.08108, 2022. find
  64. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023. find
  65. Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. Compositional exemplars for in-context learning. arXiv preprint arXiv:2302.05698, 2023. find
  66. Jieping Ye. Generalized low rank approximations of matrices. In Proceedings of the twenty-first international conference on Machine learning, pp. 112, 2004. find
  67. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284, 2023.
  68. Wenqi Zhang, Yongliang Shen, Yanna Ma, Xiaoxia Cheng, Zeqi Tan, Qingpeng Nong, and Weiming Lu. Multi-view reasoning: Consistent contrastive learning for math word problem. arXiv preprint arXiv:2210.11694, 2022a. find
  69. Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022b.
  70. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. Calibrate before use: Improving few-shot performance of language models. In Marina Meila and Tong Zhang (eds.), Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139…
  71. PMLR, 2021. URL http://proceedings.mlr.press/v139/zhao21c.html. ↗
  72. Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. Progressive-hint prompting improves reasoning in large language models. arXiv preprint arXiv:2304.09797, 2023. find
  73. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al. Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625, 2022.

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