Significant improvements have been observed in the zero-shot capabilities of the Large Language Models (LLMs). Due to their high sensitivity to input, research has increasingly focused on enhancing LLMs' performance via direct and simple prompt engineering rather than intricate domain adaptation. Studies suggest that LLMs exhibit emotional intelligence, and both positive and negative emotions can potentially enhance task performances. However, prior interaction prompts have predominantly concentrated on a single stimulus type, neglecting to compare different stimulus effects, examine the influence of varying task difficulties, or explore underlying mechanisms. This paper, inspired by the positive correlation between self-efficacy and task performance within the social cognitive theory, introduces Verbal Efficacy Stimulations (VES). Our VES comprises three types of verbal prompts: encouraging, provocative, and critical, addressing six aspects such as helpfulness and competence. And we further categorize task difficulty, aiming to extensively investigate how distinct VES influence the self-efficacy and task achievements of language models at varied levels of difficulty. The experimental results show that the three types of VES improve the performance of LLMs on most tasks, and the most effective VES varies for different models. In extensive experiments, we have obtained some findings consistent with psychological theories, providing novel insights for future research.

翻译：大型语言模型（LLMs）的零样本能力已展现出显著提升。鉴于其对输入的高度敏感性，研究日益关注通过直接而简洁的提示工程来增强LLMs的性能，而非依赖复杂的领域适应方法。研究表明，LLMs展现出情感智能，积极与消极情绪均可能提升其任务表现。然而，先前的交互提示主要集中于单一刺激类型，未能比较不同刺激效果、考察任务难度变化的影响或探索其内在机制。本文受社会认知理论中自我效能感与任务表现正相关关系的启发，提出了言语效能激励（VES）。我们的VES包含鼓励型、激发型与批判型三类言语提示，涵盖助益性、能力等六个维度。我们进一步对任务难度进行分类，旨在深入探究不同VES如何影响语言模型在不同难度层级下的自我效能感与任务成就。实验结果表明，三类VES在多数任务上提升了LLMs的性能，且最有效的VES因模型而异。在大量实验中，我们获得了一些与心理学理论相符的发现，为未来研究提供了新的见解。