Original title: Speak Like a Native: Prompting Large Language Models in a Native Style
Authors: Zhicheng Yang, Yiwei Wang, Yinya Huang, Jing Xiong, Xiaodan Liang, Jing Tang
In the article, researchers explore how different styles of writing influence the performance of big language models. They introduce a new method called AlignCoT, designed to boost these models’ reasoning by matching their style with the way they naturally talk. Unlike other techniques, AlignCoT focuses on making the examples given to these models match their unique “native” style, which hasn’t been fully studied before. By aligning the writing style in these examples, the researchers found that the models perform better at reasoning tasks. They tested this method extensively and found a significant improvement in performance, even beating carefully handcrafted examples by 2.5% on a specific benchmark. AlignCoT also works well when combined with other advanced techniques, consistently enhancing the models’ performance. The researchers are making their code and data available for others to use.
Original article: https://arxiv.org/abs/2311.13538