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Large Language Models are Zero-Shot Reasoners | Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step a... | Experimental results demonstrate that the Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics, symbolic reasoning, and other logical reasoning tasks, without any hand-crafted few-shot examples. | ## Large Language Models are Zero-Shot Reasoners
**Takeshi Kojima**
The University of Tokyo
```
t.kojima@weblab.t.u-tokyo.ac.jp
```
**Shixiang Shane Gu**
Google Research, Brain Team
**Machel Reid** **Yutaka Matsuo** **Yusuke Iwasawa**
Google Research[∗] The University of Tokyo The University of Tokyo
**Abstract*... | [
"Yutaka, Matsuo",
"Takeshi, Kojima",
"Shixiang Shane, Gu",
"Machel, Reid",
"Yusuke, Iwasawa"
] | 2022-05-24T00:00:00 | NeurIPS 2022 Poster | true | 1,000 | 75 | null | https://arxiv.org/abs/2205.11916v4 | https://arxiv.org/abs/2205.11916 | https://www.semanticscholar.org/paper/e7ad08848d5d7c5c47673ffe0da06af443643bda |
Measuring Massive Multitask Language Understanding | "We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks includ(...TRUNCATED) | "While most recent models have near random-chance accuracy, the very largest GPT-3 model improves ov(...TRUNCATED) | "## MEASURING MASSIVE MULTITASK LANGUAGE UNDERSTANDING\n\n**Dan Hendrycks** **Collin Burns** **Steve(...TRUNCATED) | ["Dan, Hendrycks","Andy, Zou","Collin, Burns","Dawn, Song","Mantas, Mazeika","Steven, Basart","Jacob(...TRUNCATED) | 2021-01-12T00:00:00 | ICLR 2021 | true | 1,000 | 23 | null | http://arxiv.org/abs/2009.03300 | https://arxiv.org/abs/2009.03300 | https://www.semanticscholar.org/paper/814a4f680b9ba6baba23b93499f4b48af1a27678 |
ReAct: Synergizing Reasoning and Acting in Language Models | "While large language models (LLMs) have demonstrated impressive capabilities across tasks in langua(...TRUNCATED) | "The use of LLMs are explored to generate both reasoning traces and task-specific actions in an inte(...TRUNCATED) | "## REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS\n\nShunyu Yao*,1[∗], Jeffrey Zhao2,(...TRUNCATED) | ["Shunyu, Yao","Karthik, Narasimhan","Dian, Yu","Jeffrey, Zhao","Izhak, Shafran","Yuan, Cao","Nan, D(...TRUNCATED) | 2023-03-09T00:00:00 | ICLR 2023 | true | 1,000 | 31 | null | http://arxiv.org/abs/2210.03629 | https://arxiv.org/abs/2210.03629 | https://www.semanticscholar.org/paper/99832586d55f540f603637e458a292406a0ed75d |
Self-Consistency Improves Chain of Thought Reasoning in Language Models | "Chain-of-thought prompting combined with pretrained large language models has achieved encouraging (...TRUNCATED) | "This paper proposes a new decoding strategy, self-consistency, to replace the naive greedy decoding(...TRUNCATED) | "## SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS\n\n**Xuezhi Wang[†‡](...TRUNCATED) | ["Jason, Wei","Aakanksha, Chowdhery","Xuezhi, Wang","Denny, Zhou","Dale, Schuurmans","Ed, Chi","Quoc(...TRUNCATED) | 2023-03-07T00:00:00 | ICLR 2023 | true | 1,000 | 90 | null | http://arxiv.org/abs/2203.11171 | https://arxiv.org/abs/2203.11171 | https://www.semanticscholar.org/paper/5f19ae1135a9500940978104ec15a5b8751bc7d2 |
Toolformer: Language Models Can Teach Themselves to Use Tools | "Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or t(...TRUNCATED) | "This paper introduces Toolformer, a model trained to decide which APIs to call, when to call them, (...TRUNCATED) | "## Toolformer: Language Models Can Teach Themselves to Use Tools\n\n**Timo Schick** **Jane Dwivedi-(...TRUNCATED) | ["Timo, Schick","Jane, Dwivedi-Yu","Roberto, Dessì","Roberta, Raileanu","Maria, Lomeli","Luke, Zett(...TRUNCATED) | 2023-02-09T00:00:00 | NeurIPS 2023 Oral | true | 1,000 | 23 | null | http://arxiv.org/abs/2302.04761 | https://arxiv.org/abs/2302.04761 | https://www.semanticscholar.org/paper/53d128ea815bcc0526856eb5a9c42cc977cb36a7 |
Training Verifiers to Solve Math Word Problems | "State-of-the-art language models can match human performance on many tasks, but they still struggle(...TRUNCATED) | "It is demonstrated that verification significantly improves performance on GSM8K, and there is stro(...TRUNCATED) | "## Training Verifiers to Solve Math Word Problems\n\n\n**Karl Cobbe[∗]** **Vineet Kosaraju[∗]**(...TRUNCATED) | ["Mark, Chen","John, Schulman","Reiichiro, Nakano","Jerry, Tworek","Vineet, Kosaraju","Jacob, Hilton(...TRUNCATED) | 2021-11-17T00:00:00 | null | false | 1,000 | 130 | null | http://arxiv.org/abs/2110.14168 | https://arxiv.org/abs/2110.14168 | https://www.semanticscholar.org/paper/d6045d2ccc9c09ca1671348de86d07da6bc28eea |
Tree of Thoughts: Deliberate Problem Solving with Large Language Models | "Language models are increasingly being deployed for general problem solving across a wide range of (...TRUNCATED) | "A new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the po(...TRUNCATED) | "## Tree of Thoughts: Deliberate Problem Solving with Large Language Models\n\n\n**Shunyu Yao** **Di(...TRUNCATED) | ["Shunyu, Yao","Karthik, Narasimhan","Dian, Yu","Jeffrey, Zhao","Izhak, Shafran","Thomas L., Griffit(...TRUNCATED) | 2023-05-17T00:00:00 | NeurIPS 2023 Oral | true | 1,000 | 27 | null | http://arxiv.org/abs/2305.10601 | https://arxiv.org/abs/2305.10601 | https://www.semanticscholar.org/paper/2f3822eb380b5e753a6d579f31dfc3ec4c4a0820 |
Measuring Mathematical Problem Solving With the MATH Dataset | "Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the(...TRUNCATED) | "This work introduces MATH, a new dataset of 12,500 challenging competition mathematics problems whi(...TRUNCATED) | "## Measuring Mathematical Problem Solving With the MATH Dataset\n\n**Dan Hendrycks** **Collin Burns(...TRUNCATED) | ["Dan, Hendrycks","Collin, Burns","Dawn, Song","Saurav, Kadavath","Akul, Arora","Steven, Basart","Er(...TRUNCATED) | 2021-11-08T00:00:00 | NeurIPS 2021 | true | 935 | 83 | null | http://arxiv.org/abs/2103.03874 | https://arxiv.org/abs/2103.03874 | https://www.semanticscholar.org/paper/57d1e7ac339e783898f2c3b1af55737cbeee9fc5 |
Self-Refine: Iterative Refinement with Self-Feedback | "Like humans, large language models (LLMs) do not always generate the best output on their first try(...TRUNCATED) | "Self-Refine is introduced, an approach for improving initial outputs from LLMs through iterative fe(...TRUNCATED) | "### SELF-REFINE: Iterative Refinement with Self-Feedback\n\n\n**Aman Madaan[1], Niket Tandon[2], Pr(...TRUNCATED) | ["Sean, Welleck","Aman, Madaan","Nouha, Dziri","Luyu, Gao","Bodhisattwa Prasad, Majumder","Uri, Alon(...TRUNCATED) | 2023-05-25T00:00:00 | NeurIPS 2023 Poster | true | 846 | 32 | null | http://arxiv.org/abs/2303.17651 | https://arxiv.org/abs/2303.17651 | https://www.semanticscholar.org/paper/3aaf6a2cbad5850ad81ab5c163599cb3d523436f |
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models | "Chain-of-thought prompting has demonstrated remarkable performance on various natural language reas(...TRUNCATED) | "Experimental results on tasks related to symbolic manipulation, compositional generalization, and m(...TRUNCATED) | "## LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS\n\n**Denny Zhou[∗]*(...TRUNCATED) | ["Jason, Wei","Olivier, Bousquet","Quoc V., Le","Xuezhi, Wang","Le, Hou","Nathan, Scales","Denny, Zh(...TRUNCATED) | 2022-09-29T00:00:00 | ICLR 2023 Poster | true | 788 | 50 | null | https://openreview.net/forum?id=WZH7099tgfM | https://arxiv.org/abs/2205.10625 | https://www.semanticscholar.org/paper/5437e8adab596d7294124c0e798708e050e25321 |
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