1 citations · 1 across the 16 of their papers we have counts for
17 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…
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
Mengze Hong, Di Jiang, Chen Jason Zhang +5
Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistanc…
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,…
Contextualized Token Discrimination for Speech Search Query Correction
Junyu Lu, Di Jiang, Mengze Hong +3
Query spelling correction is an important function of modern search engines since it effectively helps users express their intentions clearly. With the growing popularity of speech…
Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Mengze Hong, Chen Jason Zhang, Di Jiang
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effec…
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…
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…