9 papers
Behavior Tokens Speak Louder: Disentangled Explainable Recommendation with Behavior Vocabulary
Xinshun Feng, Mingzhe Liu, Yi Qiao +3
Recent advances in explainable recommendations have explored the integration of language models to analyze natural language rationales for user-item interactions. Despite their pot…
Supervised Fine Tuning of Large Language Models for Domain Specific Knowledge Graph Construction:A Case Study on Hunan's Historical Celebrities
Junjie Hao, Chun Wang, Ying Qiao +4
Large language models and knowledge graphs offer strong potential for advancing research on historical culture by supporting the extraction, analysis, and interpretation of cultura…
SERL: Self-Examining Reinforcement Learning on Open-Domain
Weixuan Ou, Yanzhao Zheng, Shuoshuo Sun +7
Reinforcement Learning (RL) has been shown to improve the capabilities of large language models (LLMs). However, applying RL to open-domain tasks faces two key challenges: (1) the…
Equip Pre-ranking with Target Attention by Residual Quantization
Yutong Li, Yu Zhu, Yichen Qiao +4
The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel…
ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan +1
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as gr…
Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting
Yufei Li, John Nham, Ganesh Jawahar +7
Generic text rewriting is a prevalent large language model (LLM) application that covers diverse real-world tasks, such as style transfer, fact correction, and email editing. These…
Optimal Brain Iterative Merging: Mitigating Interference in LLM Merging
Zhixiang Wang, Zhenyu Mao, Yixuan Qiao +2
Large Language Models (LLMs) have demonstrated impressive capabilities, but their high computational costs pose challenges for customization. Model merging offers a cost-effective…
Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data
D. Wiechmann, E. Kempa, E. Kerz +1
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances i…
MANTIS at #SMM4H 2023: Leveraging Hybrid and Ensemble Models for Detection of Social Anxiety Disorder on Reddit
Sourabh Zanwar, Daniel Wiechmann, Yu Qiao +1
This paper presents our system employed for the Social Media Mining for Health 2023 Shared Task 4: Binary classification of English Reddit posts self-reporting a social anxiety dis…