7 papers
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
Xuxing Chen, Yun He, Jiayi Xu +9
In machine learning, the goal of multi-task learning (MTL) is to optimize multiple objectives together. Recent works, for example, Multiple Gradient Descent Algorithm (MGDA) and it…
CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production
Yixin Nie, Lin Guan, Zhongyao Ma +19
This report presents CharacterFlywheel, an iterative flywheel process for improving large language models (LLMs) in production social chat applications across Instagram, WhatsApp,…
Preference Discerning with LLM-Enhanced Generative Retrieval
Fabian Paischer, Liu Yang, Linfeng Liu +12
In sequential recommendation, models recommend items based on user's interaction history. To this end, current models usually incorporate information such as item descriptions and…
APOLLO: SGD-like Memory, AdamW-level Performance
Hanqing Zhu, Zhenyu Zhang, Wenyan Cong +7
Large language models (LLMs) are notoriously memory-intensive during training, particularly with the popular AdamW optimizer. This memory burden necessitates using more or higher-e…
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking
Ilqar Ramazanli, Hamid Eghbalzadeh, Xiaoyi Liu +6
Perturbation-based regularization techniques address many challenges in industrial-scale large models, particularly with sparse labels, and emphasize consistency and invariance for…