97 citations · 400 across the 54 of their papers we have counts for
9 papers · 1 filter
Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
Yanming Sun, Runzhe Zhan, Chi Seng Cheang +7
\textbf{RE}trieval-\textbf{A}ugmented \textbf{L}LM-based \textbf{M}achine \textbf{T}ranslation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but…
Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
Runzhe Zhan, Zhihong Huang, Xinyi Yang +3
Recent advancements in large reasoning models (LRMs) have introduced an intermediate "thinking" process prior to generating final answers, improving their reasoning capabilities on…
Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing
Fengying Ye, Shanshan Wang, Lidia S. Chao +1
Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor p…
Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry
Shanshan Wang, Junchao Wu, Fengying Ye +3
The rapid development of advanced large language models (LLMs) has made AI-generated text indistinguishable from human-written text. Previous work on detecting AI-generated text ha…
RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang +7
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well,…
SGIC: A Self-Guided Iterative Calibration Framework for RAG
Guanhua Chen, Yutong Yao, Lidia S. Chao +2
Recent research in retrieval-augmented generation (RAG) has concentrated on retrieving useful information from candidate documents. However, numerous methodologies frequently negle…
Not All LoRA Parameters Are Essential: Insights on Inference Necessity
Guanhua Chen, Yutong Yao, Ci-Jun Gao +3
Current research on LoRA primarily focuses on minimizing the number of fine-tuned parameters or optimizing its architecture. However, the necessity of all fine-tuned LoRA layers du…
Rethinking Prompt-based Debiasing in Large Language Models
Xinyi Yang, Runzhe Zhan, Derek F. Wong +3
Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on th…
Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models
Yuyi Huang, Runzhe Zhan, Derek F. Wong +2
Large language models (LLMs) have significantly influenced various industries but suffer from a critical flaw, the potential sensitivity of generating harmful content, which poses…