Summary of Dial-in Llm: Human-aligned Llm-in-the-loop Intent Clustering For Customer Service Dialogues, by Mengze Hong et al.
Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
by Mengze Hong, Di Jiang, Yuanfeng Song, Lu Wang, Wailing Ng, Yanjie Sun, Chen Jason Zhang, Qing Li
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an LLM-in-the-loop (LLM-ITL) intent clustering framework to discover customer intentions in dialogue conversations. Existing methods often fail to align with human perceptions due to the reliance on embedding distance metrics and sentence embeddings. The proposed approach integrates the semantic understanding capabilities of LLMs, achieving over 95% accuracy in fine-tuning for semantic coherence evaluation and intent cluster naming. The paper also designs an LLM-ITL clustering algorithm and proposes task-specific techniques tailored for customer service dialogue intent clustering. To evaluate these approaches, a comprehensive Chinese dialogue intent dataset is introduced, comprising over 100,000 real customer service calls and 1,507 human-annotated intent clusters. The results show that the proposed approaches significantly outperform LLM-guided baselines, achieving notable improvements in clustering quality and a 12% boost in the downstream intent classification task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out what customers want when they talk to automated service agents. Right now, most methods don’t quite match how humans think about things because they rely too much on math and computers. The researchers suggest using special language models (LLMs) to help find patterns in customer conversations. They test their ideas by training the LLMs to understand what customers are saying, and then use that understanding to group similar conversations together. To make sure their methods work well, they create a big dataset of real customer service calls with labels showing what kind of conversation each one is. The results show that using LLMs really helps improve how well computers can understand customer intentions. |
Keywords
» Artificial intelligence » Classification » Clustering » Embedding » Fine tuning
