TORSO：面向通用任务的模板导向推理
TORSO: Template-Oriented Reasoning Towards General Tasks
AI总结：
针对少样本提示依赖示例且成本高的问题，提出TORSO框架，引导LLMs利用内部推理能力在无需手工示例的情况下处理多任务，在多个基准上表现优异。
摘要：
引导大型语言模型（LLMs）在生成响应时模拟人类推理的方法，已成为使其逐步解决复杂问题并获得卓越性能的有效手段。然而，现有使用少样本提示生成响应的方法严重依赖所提供的示例，限制了模型内在推理能力的发挥。此外，构建特定任务的少样本提示通常成本高昂，且可能导致不同任务间的不一致。在本研究中，我们提出了面向模板的推理（TORSO），它引导模型利用内部推理能力在各种任务中生成恰当响应，而无需手动制作少样本示例。实验结果表明，TORSO在多样化的LLMs基准上取得了强劲的性能，并提供了合理的依据。
英文摘要：
The approaches that guide Large Language Models (LLMs) to emulate human reasoning during response generation have emerged as an effective method for enabling them to solve complex problems in a step-by-step manner, thereby achieving superior performance. However, most existing approaches using few-shot prompts to generate responses heavily depend on the provided examples, limiting the utilization of the model's inherent reasoning capabilities. Moreover, constructing task-specific few-shot prompts is often costly and may lead to inconsistencies across different tasks. In this work, we introduce Template-Oriented Reasoning (TORSO), which elicits the model to utilize internal reasoning abilities to generate proper responses across various tasks without the need for manually crafted few-shot examples. Our experimental results demonstrate that TORSO achieves strong performance on diverse LLMs benchmarks with reasonable rationales.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
