Strategy-Induct: Task-Level Strategy Induction for Instruction Generation

• URL: http://arxiv.org/abs/2605.20924v1
• Date: Wed, 20 May 2026 09:10:43 GMT
• Title: Strategy-Induct: Task-Level Strategy Induction for Instruction Generation
• Abstract summary: Strategy-Induct is a framework that derives task-level instructions solely from a small set of example questions.<n>Our approach first prompts the model to generate explicit reasoning strategies for each question, forming (strategy, question) pairs.<n>These pairs are then used to induce a task instruction that guides reasoning.
• Score: 23.56553381765231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction induction demonstrates that LLMs can infer better instructions with limited examples, existing approaches often rely on input-output pairs, where obtaining labeled answers can be difficult or costly. To address this limitation, we propose Strategy-Induct, a framework that derives task-level instructions solely from a small set of example questions without requiring labeled answers. Our approach first prompts the model to generate explicit reasoning strategies for each question, forming (strategy, question) pairs. These pairs are then used to induce a task instruction that guides reasoning. Experiments across multiple tasks and model scales demonstrate that Strategy-Induct outperforms state-of-the-art methods in question-only settings. Furthermore, we observe that jointly utilizing LLMs and Large Reasoning Models across task instruction generation and inference may lead to further performance improvements.

Related papers

• DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding [63.257540233507626]
  We propose a paradigm that requires the model to execute a structured Analysis, Localization and Reasoning workflow.<n>We show it robustly generalizes from short-page training to ultra-long documents and is naturally synergistic with visual Retrieval-Augmented Generation systems.
  arXiv  Detail & Related papers  (2026-04-14T14:39:26Z)
• Zero-Shot Instruction Following in RL via Structured LTL Representations [54.08661695738909]
  Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents.<n>Recent work has shown that interpreting instructions as finite automata, which can be seen as high-level programs monitoring task progress, enables learning a single generalist policy capable of executing arbitrary instructions at test time.<n>We propose a novel approach to learning a multi-task policy for following arbitrary instructions that addresses this shortcoming.
  arXiv  Detail & Related papers  (2025-12-02T10:44:51Z)
• TORSO: Template-Oriented Reasoning Towards General Tasks [23.681707595200265]
  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.
  arXiv  Detail & Related papers  (2025-09-11T13:31:35Z)
• Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks [40.48180253367968]
  We introduce an in-context learning methodology that improves the generalization capabilities of general purpose LLMs.<n>Our approach employs an iterative example selection strategy, which incrementally constructs a tailored set of few-shot examples.<n>Our experiments reveal that some LLMs achieve better generalization performances when prompted with simpler few-shot examples.
  arXiv  Detail & Related papers  (2025-09-01T08:54:45Z)
• Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning [19.75678229122211]
  Large Language Models (LLMs) have achieved remarkable success across various domains.<n>They still face significant challenges, including high computational costs for training and limitations in solving complex reasoning problems.<n>We propose a novel framework that leverages graph learning to enable more flexible and adaptive reasoning capabilities.
  arXiv  Detail & Related papers  (2025-05-09T02:51:22Z)
• Learning Task Representations from In-Context Learning [67.66042137487287]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL)<n>We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads.<n>The proposed method successfully extracts task-specific information from in-context demonstrations and excels in both text and regression tasks.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities [51.594836904623534]
  We investigate whether instruction-tuned models possess fundamentally different capabilities from base models that are prompted using in-context examples.<n>We show that the performance of instruction-tuned models is significantly correlated with the in-context performance of their base counterparts.<n>Specifically, we extend this understanding to instruction-tuned models, suggesting that their pretraining data similarly sets a limiting boundary on the tasks they can solve.
  arXiv  Detail & Related papers  (2025-01-15T10:57:55Z)
• Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation [68.58373854950294]
  We focus on causal reasoning and address the task of establishing causal relationships based on correlation information.<n>We introduce a prompting strategy for this problem that breaks the original task into fixed subquestions.<n>We evaluate our approach on an existing causal benchmark, Corr2Cause.
  arXiv  Detail & Related papers  (2024-12-18T15:32:27Z)
• Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization [19.200989737492595]
  Large language models (LLMs) have shown great progress in responding to user questions.<n>The quality of LLM outputs heavily depends on the prompt design, where a good prompt might enable the LLM to answer a very challenging question correctly.<n>We propose a hierarchy of LLMs, first constructing a prompt with precise instructions and accurate wording in a hierarchical manner, and then using this prompt to generate the final answer to the user query.
  arXiv  Detail & Related papers  (2024-05-30T17:05:45Z)
• CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems [56.302581679816775]
  This paper proposes Comprehensive Instruction (CINS) that exploits PLMs with task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD. Experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data.
  arXiv  Detail & Related papers  (2021-09-10T03:23:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.