39 citations · 96 across the 38 of their papers we have counts for
36 papers · 1 filter
SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context
Qinkai Zhang, Yanyan Zhao, Xin Lu +3
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive person…
ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents
Xing Fu, Yulin Hu, Mengtong Ji +5
Memory-augmented language agents are increasingly deployed in affective applications such as emotional support, where understanding and responding to users' latent emotional needs…
ConflictBench: Evaluating Human-AI Conflict via Interactive and Visually Grounded Environments
Weixiang Zhao, Haozhen Li, Yanyan Zhao +5
As large language models (LLMs) evolve into autonomous agents capable of acting in open-ended environments, ensuring behavioral alignment with human values becomes a critical safet…
OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
Yulin Hu, Zimo Long, Jiahe Guo +5
Memory-augmented conversational agents enable personalized interactions using long-term user memory and have gained substantial traction. However, existing benchmarks primarily foc…
Large Language Model Agents Are Not Always Faithful Self-Evolvers
Weixiang Zhao, Yingshuo Wang, Yichen Zhang +5
Self-evolving large language model (LLM) agents continually improve by accumulating and reusing past experience, yet it remains unclear whether they faithfully rely on that experie…
MPO: Multilingual Safety Alignment via Reward Gap Optimization
Weixiang Zhao, Yulin Hu, Yang Deng +8
Large language models (LLMs) have become increasingly central to AI applications worldwide, necessitating robust multilingual safety alignment to ensure secure deployment across di…
How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation
Xin Lu, Yanyan Zhao, Si Wei +3
Pre-trained language models represented by the Transformer have been proven to possess strong base capabilities, and the representative self-attention mechanism in the Transformer…
Teaching Language Models to Evolve with Users: Dynamic Profile Modeling for Personalized Alignment
Weixiang Zhao, Xingyu Sui, Yulin Hu +6
Personalized alignment is essential for enabling large language models (LLMs) to engage effectively in user-centric dialogue. While recent prompt-based and offline optimization met…
When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners
Weixiang Zhao, Jiahe Guo, Yang Deng +9
Multilingual reasoning remains a significant challenge for large language models (LLMs), with performance disproportionately favoring high-resource languages. Drawing inspiration f…
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter
Weixiang Zhao, Xingyu Sui, Xinyang Han +9
The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they fac…
Beware of Your Po! Measuring and Mitigating AI Safety Risks in Role-Play Fine-Tuning of LLMs
Weixiang Zhao, Yulin Hu, Yang Deng +8
Role-playing enables large language models (LLMs) to engage users in immersive and personalized interactions, but it also introduces significant safety risks. Existing role-play fi…
Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis
Hao Yang, Zhenyu Zhang, Yanyan Zhao +1
As a fine-grained task, multimodal aspect-based sentiment analysis (MABSA) mainly focuses on identifying aspect-level sentiment information in the text-image pair. However, we obse…
Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models
Di Wu, Xin Lu, Yanyan Zhao +1
Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often co…
Lens: Rethinking Multilingual Enhancement for Large Language Models
Weixiang Zhao, Yulin Hu, Jiahe Guo +7
As global demand for multilingual large language models (LLMs) grows, most LLMs still remain overly focused on English, leading to the limited access to advanced AI for non-English…
RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models
Bichen Wang, Yuzhe Zi, Yixin Sun +2
With the passage of the Right to Be Forgotten (RTBF) regulations and the scaling up of language model training datasets, research on model unlearning in large language models (LLMs…
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Hao Yang, Yanyan Zhao, Yang Wu +6
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and…
Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching
Weixiang Zhao, Yulin Hu, Zhuojun Li +7
Safety alignment of large language models (LLMs) has been gaining increasing attention. However, current safety-aligned LLMs suffer from the fragile and imbalanced safety mechanism…
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
Xin Lu, Yanyan Zhao, Bing Qin +3
Pre-trained language models have been proven to possess strong base capabilities, which not only excel in in-distribution language modeling but also show powerful abilities in out-…
Improving the Downstream Performance of Mixture-of-Experts Transformers via Weak Vanilla Transformers
Xin Lu, Yanyan Zhao, Bing Qin +1
Recently, Mixture of Experts (MoE) Transformers have garnered increasing attention due to their advantages in model capacity and computational efficiency. However, studies have ind…
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence
Weixiang Zhao, Zhuojun Li, Shilong Wang +5
Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the c…
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Weixiang Zhao, Shilong Wang, Yulin Hu +6
The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific k…
An Early Evaluation of GPT-4V(ision)
Yang Wu, Shilong Wang, Hao Yang +4
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such a…
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
Hao Yang, Can Gao, Hao Líu +3
Vision-and-language (VL) pre-training, which aims to learn a general representation of image-text pairs that can be transferred to various vision-and-language tasks. Compared with…
Improving Cross-Task Generalization with Step-by-Step Instructions
Yang Wu, Yanyan Zhao, Zhongyang Li +2
Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target ta…