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    <abstract>Continuous instruction following closely mirrors real-world tasks by requiring models to solve sequences of interdependent steps, yet existing multi-step instruction datasets suffer from three key limitations: (1) lack of logical coherence across turns, (2) narrow topical breadth and depth, and (3) reliance on rigid templates or heavy manual effort. We introduce LoCt-Pipeline, a novel pipeline that leverages modern LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues, producing chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. We employed this pipeline to construct LoCt-Instruct for assessing models’ problem-solving abilities. The generated chains serve as a testbed for benchmarking a variety of models, including reasoning-oriented architectures, instruction-tuned variants, and state-of-the-art closed-source LLMs on their capacity to follow and correctly respond to each step. Our results reveal a substantial performance gap between current LLMs and human solvers. These findings highlight the need for more robust continuous instruction following. We publicly release the dataset and end-to-end pipeline.</abstract>
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%0 Conference Proceedings
%T LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions
%A Sun, Hongyu
%A Sakai, Yusuke
%A Sakajo, Haruki
%A Ozaki, Shintaro
%A Hayashi, Kazuki
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F sun-etal-2025-loct
%X Continuous instruction following closely mirrors real-world tasks by requiring models to solve sequences of interdependent steps, yet existing multi-step instruction datasets suffer from three key limitations: (1) lack of logical coherence across turns, (2) narrow topical breadth and depth, and (3) reliance on rigid templates or heavy manual effort. We introduce LoCt-Pipeline, a novel pipeline that leverages modern LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues, producing chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. We employed this pipeline to construct LoCt-Instruct for assessing models’ problem-solving abilities. The generated chains serve as a testbed for benchmarking a variety of models, including reasoning-oriented architectures, instruction-tuned variants, and state-of-the-art closed-source LLMs on their capacity to follow and correctly respond to each step. Our results reveal a substantial performance gap between current LLMs and human solvers. These findings highlight the need for more robust continuous instruction following. We publicly release the dataset and end-to-end pipeline.
%U https://aclanthology.org/2025.emnlp-main.1734/
%P 34187-34206

Markdown (Informal)

[LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions](https://aclanthology.org/2025.emnlp-main.1734/) (Sun et al., EMNLP 2025)

• LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions (Sun et al., EMNLP 2025)

ACL

• Hongyu Sun, Yusuke Sakai, Haruki Sakajo, Shintaro Ozaki, Kazuki Hayashi, Hidetaka Kamigaito, and Taro Watanabe. 2025. LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34187–34206, Suzhou, China. Association for Computational Linguistics.