In-Context Learning (ICL) approaches, such as Zero-Shot and Few-Shot prompting, allow Large Language Models (LLMs) to tackle reasoning tasks without additional fine-tuning. However, Zero-Shot prompting often struggles with more complex tasks, whereas Few-Shot prompting demands considerable manual effort and domain expertise to design effective prompts. Although existing work has attempted to alleviate these issues by extracting reasoning rules from carefully crafted, task-specific representative examples, creating or obtaining such examples can be impractical in real-world scenarios. In this paper, we propose a novel approach that enhances the inference accuracy by injecting reasoning principles extracted from QA data, without relying on representative Few-Shot exemplars. This offers a lightweight yet adaptive way to boost accuracy on complex reasoning tasks, while avoiding manual effort and the high exploration costs typical of prior methods. Experiments on benchmarks show that, using GPT-4o, our method outperforms similarity-based Few-Shot and Zero-Shot prompting methods on challenging benchmarks such as GPQA-diamond, achieving an absolute accuracy improvement of up to 2% in scenarios where carefully crafted Few-Shot examples are unavailable.

Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots employed responsibly for social good can be an enabler of positive individual and social change. Existing methods rely on fine-tuning persuasive chatbots with task-specific training data which is costly, if not infeasible, to collect. Furthermore, they employ only a handful of pre-defined persuasion strategies. We propose PersuaBot, a zero-shot chatbot based on Large Language Models (LLMs) that is factual and more persuasive by leveraging many more nuanced strategies. PersuaBot uses an LLM to first generate a natural responses, from which the strategies used are extracted. To combat hallucination of LLMs, Persuabot replace any unsubstantiated claims in the response with retrieved facts supporting the extracted strategies. We applied our chatbot, PersuaBot, to three significantly different domains needing persuasion skills: donation solicitation, recommendations, and health intervention. Our experiments on simulated and human conversations show that our zero-shot approach is more persuasive than prior work, while achieving factual accuracy surpassing state-of-the-art knowledge-oriented chatbots.