9 citations · 37 across the 18 of their papers we have counts for
18 papers
Deep Research: A Systematic Survey
Zhengliang Shi, Yiqun Chen, Haitao Li +23
Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable ou…
Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs
Jen-Tse Huang, Dasen Dai, Jen-Yuan Huang +7
Humans develop perception through a bottom-up hierarchy: from basic primitives and Gestalt principles to high-level semantics. Current Multimodal Large Language Models (MLLMs) are…
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao +5
Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. However, ensuring the s…
On the Shortcut Learning in Multilingual Neural Machine Translation
Wenxuan Wang, Wenxiang Jiao, Jen-tse Huang +2
In this study, we revisit the commonly-cited off-target issue in multilingual neural machine translation (MNMT). By carefully designing experiments on different MNMT scenarios and…
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization
Hexuan Deng, Wenxiang Jiao, Xuebo Liu +3
Large language models (LLMs) deliver impressive results but face challenges from increasing model sizes and computational costs. Structured pruning reduces model size and speeds up…
NewTerm: Benchmarking Real-Time New Terms for Large Language Models with Annual Updates
Hexuan Deng, Wenxiang Jiao, Xuebo Liu +2
Despite their remarkable abilities in various tasks, large language models (LLMs) still struggle with real-time information (e.g., new facts and terms) due to the knowledge cutoff…
Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs
Xiaoyuan Liu, Wenxuan Wang, Youliang Yuan +4
This paper explores the problem of commonsense level vision-knowledge conflict in Multimodal Large Language Models (MLLMs), where visual information contradicts model's internal co…
Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step
Wenxuan Wang, Kuiyi Gao, Youliang Yuan +5
Text-based image generation models, such as Stable Diffusion and DALL-E 3, hold significant potential in content creation and publishing workflows, making them the focus in recent…
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang +5
This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which…
CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration
Xinming Hou, Mingming Yang, Wenxiang Jiao +3
Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In t…
How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments
Jen-tse Huang, Eric John Li, Man Ho Lam +7
Decision-making is a complex process requiring diverse abilities, making it an excellent framework for evaluating Large Language Models (LLMs). Researchers have examined LLMs' deci…
Unsupervised Sign Language Translation and Generation
Zhengsheng Guo, Zhiwei He, Wenxiang Jiao +6
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns fro…
Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models
Zhiwei He, Binglin Zhou, Hongkun Hao +5
Text watermarking technology aims to tag and identify content produced by large language models (LLMs) to prevent misuse. In this study, we introduce the concept of cross-lingual c…
Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models
Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling +7
Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone…
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Tian Liang, Zhiwei He, Jen-tse Huang +7
The automatic evaluation of LLM-based agent intelligence is critical in developing advanced LLM-based agents. Although considerable effort has been devoted to developing human-anno…
Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models
Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang +4
This paper identifies a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g., ChatGPT). LLMs often provid…
Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench
Jen-tse Huang, Wenxuan Wang, Eric John Li +6
Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical med…
All Languages Matter: On the Multilingual Safety of Large Language Models
Wenxuan Wang, Zhaopeng Tu, Chang Chen +4
Safety lies at the core of developing and deploying large language models (LLMs). However, previous safety benchmarks only concern the safety in one language, e.g. the majority lan…