Chain-of-Interactions: Multi-step Iterative ICL Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational AI Interactions (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have introduced paradigm-shifting approaches in natural language processing, yet their transformative in-context learning (ICL) capabilities remain underutilized, especially in customer service dialogue summarization. |
| Approach: | They propose a single-instance, multi-step framework that orchestrates information extraction, self-correction, and evaluation through sequential interactive generation chains. |
| Outcome: | The proposed framework outperforms existing models and prompts in the customer service dialogue summarization domain. |
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Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley
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| Challenge: | In-context learning and CoT are still poorly understood, but the precise mechanisms and architectural factors driving ICL and Co T are still unclear. |
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