LogiCoT: Logical Chain-of-Thought Instruction Tuning
Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, Yue Zhang
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.- Anthology ID:
- 2023.findings-emnlp.191
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2908–2921
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.191
- DOI:
- 10.18653/v1/2023.findings-emnlp.191
- Cite (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.
- Cite (Informal):
- LogiCoT: Logical Chain-of-Thought Instruction Tuning (Liu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.191.pdf