5 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…
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…
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…
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…
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-…