5 papers
Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
Sirui Chen, Yunzhe Qi, Mengting Ai +4
Supervised fine-tuning (SFT) relies critically on selecting training data that most benefits a model's downstream performance. Gradient-based data selection methods such as TracIn…
Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decoding
Yunkai Zhang, Qiang Zhang, Feng Lin +7
Optimizing recommender systems for objectives beyond accuracy, such as diversity, novelty, and personalization, is crucial for long-term user satisfaction. To this end, industrial…
Haystack Engineering: Context Engineering for Heterogeneous and Agentic Long-Context Evaluation
Mufei Li, Dongqi Fu, Limei Wang +10
Modern long-context large language models (LLMs) perform well on synthetic "needle-in-a-haystack" (NIAH) benchmarks, but such tests overlook how noisy contexts arise from biased re…
Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
Jiaru Zou, Yikun Ban, Zihao Li +4
Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation…
Fair Anomaly Detection For Imbalanced Groups
Ziwei Wu, Lecheng Zheng, Yuancheng Yu +3
Anomaly detection (AD) has been widely studied for decades in many real-world applications, including fraud detection in finance, and intrusion detection for cybersecurity, etc. Du…