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Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
Mengze Hong, Wailing Ng, Chen Jason Zhang +2
Discovering customer intentions is crucial for automated service agents, yet existing intent clustering methods often fall short due to their reliance on embedding distance metrics…
Dialogue Language Model with Large-Scale Persona Data Engineering
Mengze Hong, Chen Jason Zhang, Chaotao Chen +2
Maintaining persona consistency is paramount in the application of open-domain dialogue systems, as exemplified by models like ChatGPT. Despite significant advancements, the limite…
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing
Mengze Hong, Di Jiang, Yuanfeng Song +1
With the growing importance of customer service in contemporary business, recognizing the intents behind service dialogues has become essential for the strategic success of enterpr…
Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
Longyu Feng, Mengze Hong, Chen Jason Zhang
Batch prompting is a common technique in large language models (LLMs) used to process multiple inputs simultaneously, aiming to improve computational efficiency. However, as batch…
Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models
Mengze Hong, Chen Jason Zhang, Di Jiang +1
Retrieval-based chatbots leverage human-verified Q\&A knowledge to deliver accurate, verifiable responses, making them ideal for customer-centric applications where compliance with…
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries
Mengze Hong, Chen Jason Zhang, Lingxiao Yang +2
Understanding the meaning of infant cries is a significant challenge for young parents in caring for their newborns. The presence of background noise and the lack of labeled data p…
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search
Mengze Hong, Di Jiang, Zichang Guo +1
Semantic Embedding Models (SEMs) have become a core component in information retrieval and natural language processing due to their ability to model semantic relevance. However, de…