Positive thinking is thought to be an important component of self-motivation
in various practical fields such as education and the workplace. Previous work,
including sentiment transfer and positive reframing, has focused on the
positive side of language. However, self-motivation that drives people to reach
their goals has not yet been studied from a computational perspective.
Moreover, negative feedback has not yet been explored, even though positive and
negative feedback are both necessary to grow self-motivation. To facilitate
self-motivation, we propose CArrot and STICk (CASTIC) dataset, consisting of
12,590 sentences with 5 different strategies for enhancing self-motivation. Our
data and code are publicly available at here.

积极思考被认为是自我激励的重要组成部分，在教育和工作等各个实践领域中。我们提出了 CArrot 和 STICk（CASTIC）数据集，旨在促进自我激励。

胡萝卜与大棒：通过正面与负面反馈促使自我激励

Carrot and Stick: Inducing Self-Motivation with Positive & Negative  Feedback

Large Language Models (LLMs) have demonstrated remarkable problem-solving and
basic mathematics abilities. However, their efficacy is highly contingent on
the formulation of the prompt. This study endeavors to quantify the influence
of incorporating "positive thinking" into the system message of the prompt,
then compare that to systematic prompt optimization. We assess the performance
of 60 combinations of system message snippets, tested with and without Chain of
Thought prompting, across three models with parameters ranging from 7 to 70
billion on the GSM8K dataset. Our findings reveal that results do not
universally generalize across models. In most instances, the inclusion of
"positive thinking" prompts positively affected model performance. Notably,
however, Llama2-70B exhibited an exception when not utilizing Chain of Thought,
as the optimal system message was found to be none at all. Given the
combinatorial complexity, and thus computation time, of experimenting with
hand-tuning prompts for large black-box models, we then compared the
performance of the best "positive thinking" prompt against the output of
systematic prompt optimization. We show that employing an automated prompt
optimizer emerges as the most effective method for enhancing performance, even
when working with smaller open-source models. Additionally, our findings reveal
that the highest-scoring, automatically-optimized prompt exhibits a degree of
peculiarity far beyond expectations.

该研究旨在量化将 “积极思考” 融入到提示信息中所产生的影响，并与系统性的提示优化进行比较。实验结果表明，大多数情况下，包含 “积极思考” 的提示对模型的表现起到积极的影响。然而，当不使用思路链时，Llama2-70B 模型却例外，最优系统信息竟然是没有任何信息。针对大型黑盒模型的提示手动调优的实验耗时较长，因此我们通过将自动化提示优化器的输出与最佳 “积极思考” 提示的表现进行了比较。研究结果显示，即使是在使用较小的开源模型时，采用自动化的提示优化方法也是提高性能最有效的方法。此外，研究还发现，得分最高的自动优化提示展示了超出预期的奇特特征。