Papers with reasoning-oriented architectures
LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions (2025.emnlp-main)
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Hongyu Sun, Yusuke Sakai, Haruki Sakajo, Shintaro Ozaki, Kazuki Hayashi, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing instruction following datasets lack logical coherence across turns, narrow topical breadth and heavy manual effort. |
| Approach: | They propose a pipeline that leverages LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues and produce chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. |
| Outcome: | The proposed pipeline improves the performance of existing LLMs by integrating multiple topic-related data into multi-turn dialogues without fixed templates or extensive human annotation. |