1 citations · 1 across the 13 of their papers we have counts for
14 papers
UXBench: Benchmarking User Experience in AI Assistants
Mengze Hong, Xia Zeng, Zeyang Lei +27
As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present \textbf{UXBench}, the…
Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition
Mengze Hong, Yi Gu, Di Jiang +4
Training automatic speech recognition (ASR) models increasingly relies on decentralized federated learning to ensure data privacy and accessibility, producing multiple local models…
Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework
Mengze Hong, Chen Jason Zhang, Zichang Guo +3
Customer service automation has seen growing demand within digital transformation. Existing approaches either rely on modular system designs with extensive agent orchestration or e…
RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems
Mengze Hong, Di Jiang, Jiangtao Wen +5
Hallucination is a major concern in LLM-driven service systems, necessitating explicit knowledge grounding for compliance-guaranteed responses. In this paper, we introduce Retrieva…
Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis
Mengze Hong, Di Jiang, Zeying Xie +3
As audio deepfakes transition from research artifacts to widely available commercial tools, robust biometric authentication faces pressing security threats in high-stakes industrie…
Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions
Mengze Hong, Di Jiang, Weiwei Zhao +5
While large language models (LLMs) offer promising capabilities for automating academic workflows, existing systems for academic peer review remain constrained by text-only inputs,…
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
Mengze Hong, Wailing Ng, Chen Jason Zhang +1
The rapid advancement of Chinese LLMs underscores the need for vertical-domain evaluations to ensure reliable applications. However, existing benchmarks often lack domain coverage…
Enhancing Generalization in Chain of Thought Reasoning for Smaller Models
Maxwell J. Yin, Dingyi Jiang, Yongbing Chen +2
Chain-of-Thought (CoT) reasoning in smaller language models is a challenging natural language process problem yet highly desirable in many real-life applications. Existing CoT know…
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction
Victor Junqiu Wei, Weicheng Wang, Di Jiang +2
Automatic speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications…
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…
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…