6 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks
Tianhao Li, Jingyu Lu, Chuangxin Chu +12
Large language models (LLMs) have a transformative impact on a variety of scientific tasks across disciplines including biology, chemistry, medicine, and physics. However, ensuring…
Collaborative User Prompt for Personalized Generative Recommendation
Jerome Ramos, Bin Wu, Aldo Lipani
Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generativ…
Understanding the Role of User Profile in the Personalization of Large Language Models
Bin Wu, Zhengyan Shi, Hossein A. Rahmani +2
Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks. However, the precise role of user profiles a…
Entropy-Based Decoding for Retrieval-Augmented Large Language Models
Zexuan Qiu, Zijing Ou, Bin Wu +3
Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses. Despite their success, ret…
Instruction Tuning With Loss Over Instructions
Zhengyan Shi, Adam X. Yang, Bin Wu +3
Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Model…