6 papers
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…
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…
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…
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…