@inproceedings{liu-etal-2023-logicot,
title = "{L}ogi{C}o{T}: Logical Chain-of-Thought Instruction Tuning",
author = "Liu, Hanmeng and
Teng, Zhiyang and
Cui, Leyang and
Zhang, Chaoli and
Zhou, Qiji and
Zhang, Yue",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.191",
doi = "10.18653/v1/2023.findings-emnlp.191",
pages = "2908--2921",
abstract = "Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.",
}
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<abstract>Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.</abstract>
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%0 Conference Proceedings
%T LogiCoT: Logical Chain-of-Thought Instruction Tuning
%A Liu, Hanmeng
%A Teng, Zhiyang
%A Cui, Leyang
%A Zhang, Chaoli
%A Zhou, Qiji
%A Zhang, Yue
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-logicot
%X Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
%R 10.18653/v1/2023.findings-emnlp.191
%U https://aclanthology.org/2023.findings-emnlp.191
%U https://doi.org/10.18653/v1/2023.findings-emnlp.191
%P 2908-2921
Markdown (Informal)
[LogiCoT: Logical Chain-of-Thought Instruction Tuning](https://aclanthology.org/2023.findings-emnlp.191) (Liu et al., Findings 2023)
ACL
- Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, and Yue Zhang. 2023. LogiCoT: Logical Chain-of-Thought Instruction Tuning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2908–2921, Singapore. Association for Computational Linguistics.