TORSO: Template-Oriented Reasoning Towards General Tasks

• URL: http://arxiv.org/abs/2509.09448v3
• Date: Mon, 15 Sep 2025 08:09:43 GMT
• Title: TORSO: Template-Oriented Reasoning Towards General Tasks
• Authors: Minhyuk Kim, Seungyoon Lee, Heuiseok Lim,
• Abstract summary: 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.<n>Our experimental results demonstrate that TORSO achieves strong performance on diverse LLMs benchmarks with reasonable rationales.
• Score: 23.681707595200265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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