325 citations · 624 across the 43 of their papers we have counts for
39 papers · 1 filter
Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective
Wangjie You, Xusheng Wang, Xing Wang +4
While Large Language Models (LLMs) have demonstrated advanced reasoning capabilities, their comprehensive evaluation in general Chinese-language contexts remains understudied. To b…
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards
Youliang Yuan, Qiuyang Mang, Jingbang Chen +7
In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. This is evidenced by a high…
Towards Evaluating Proactive Risk Awareness of Multimodal Language Models
Youliang Yuan, Wenxiang Jiao, Yuejin Xie +5
Human safety awareness gaps often prevent the timely recognition of everyday risks. In solving this problem, a proactive safety artificial intelligence (AI) system would work bette…
RaSA: Rank-Sharing Low-Rank Adaptation
Zhiwei He, Zhaopeng Tu, Xing Wang +7
Low-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stem…
Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation
Longyue Wang, Siyou Liu, Chenyang Lyu +11
Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation. We focus on three language…
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…
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…
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…
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
Zhiwei He, Xing Wang, Wenxiang Jiao +4
Insufficient modeling of human preferences within the reward model is a major obstacle for leveraging human feedback to improve translation quality. Fortunately, quality estimation…
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…
GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang +4
Safety lies at the core of the development of Large Language Models (LLMs). There is ample work on aligning LLMs with human ethics and preferences, including data filtering in pret…
Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench
Jen-tse Huang, Man Ho Lam, Eric John Li +5
Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has become increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psych…
Revisiting the Reliability of Psychological Scales on Large Language Models
Jen-tse Huang, Wenxiang Jiao, Man Ho Lam +3
Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behaviora…
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Tian Liang, Zhiwei He, Wenxiang Jiao +6
Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the researc…
Cross-modality Data Augmentation for End-to-End Sign Language Translation
Jinhui Ye, Wenxiang Jiao, Xing Wang +2
End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging…
Exploring Human-Like Translation Strategy with Large Language Models
Zhiwei He, Tian Liang, Wenxiang Jiao +6
Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-l…
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang +5
Large language models (LLMs) like ChatGPT have exhibited remarkable abilities on a wide range of natural language processing~(NLP) tasks, including various machine translation abil…