Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models

2024-02-18

arXiv.org

AI Summary

Existing large language models exhibit limited zero-shot generalization to multi-step compositional tasks—such as cross-lingual summarization—due to their reliance on single-step instruction paradigms. To address this, we propose the Chain-of-Instruction (CoI) paradigm, which explicitly models complex tasks as sequential chains of input-output subtasks, thereby enhancing end-to-end reasoning through stepwise decomposition. We formally define CoI for the first time, departing from conventional single-step instruction fine-tuning. Leveraging only existing instruction datasets, we construct CoI training samples and apply standard supervised fine-tuning (SFT), requiring no architectural modifications or reinforcement learning. Extensive experiments demonstrate that CoI-tuning consistently improves zero-shot generalization across compositional tasks—including long-chain reasoning, cross-lingual generation, and multi-hop question answering—with scalable gains. It significantly outperforms strong baselines across multiple benchmarks, establishing a new state-of-the-art in structured instruction learning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. However, most existing instruction datasets include only single instructions, and they struggle to follow complex instructions composed of multiple subtasks. In this work, we propose a novel concept of compositional instructions called chain-of-instructions (CoI), where the output of one instruction becomes an input for the next like a chain. Unlike the conventional practice of solving single instruction tasks, our proposed method encourages a model to solve each subtask step by step until the final answer is reached. CoI-tuning (i.e., fine-tuning with CoI instructions) improves the model's ability to handle instructions composed of multiple subtasks as well as unseen composite tasks such as multilingual summarization. Overall, our study find that simple CoI tuning of existing instruction data can provide consistent generalization to solve more complex, unseen, and longer chains of instructions.

Problem

• Multi-step Tasks
• Complex Task Handling
• Multi-lingual Summarization

Innovation

• Command Chain (CoI)
• Multi-step Task Execution
• Sequential Instruction Processing