Zeyu Zhang

UvA-DARE (Digital Academic Repository) A Deep Dive Into Cross-Dataset Entity Matching with Large and Small Language Models

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

This work provides a new method for fine-tuning models with sparse attention that works for any KV cache policy, runs on a moderate hardware budget, and allows the model to co-adapt with the policy, often outperforming models trained with exact attention (sequence parallelism).

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

The factors underlying performance differences across matcher architectures are clarified and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability.