Papers with least-to-most prompting method
Exploring Chain of Thought Style Prompting for Text-to-SQL (2023.emnlp-main)
Copied to clipboard
| Challenge: | In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. |
| Approach: | They propose a new chain of thought prompting method that enhances LLMs’ reasoning ability through chain of thinking prompting, including the original chain-of-thought prompting and least-to-most prompting. |
| Outcome: | The proposed method brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gain, versus the least-to-most prompting. |