activity
20242026
most citedAnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

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

Matthias Seeger, Zeyu Zhang, Vihang Patil +2

A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive…

cs.DB2026

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

Zeyu Zhang, Xue Li, Iacer Calixto +2

Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-encoder, and generative matcher a…

cs.DB2026

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework

Chuangtao Ma, Zeyu Zhang, Arijit Khan +2

Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or spar…

cs.CL2026

NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

Rong Fu, Yang Li, Zeyu Zhang +7

Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require prec…

cs.LG2026

MetaToolAgent: Towards Generalizable Tool Usage in LLMs through Meta-Learning

Zheng Fang, Wolfgang Mayer, Zeyu Zhang +4

Tool learning is increasingly important for large language models (LLMs) to effectively coordinate and utilize a diverse set of tools in order to solve complex real-world tasks. By…

cs.LG2025

LAMP: Extracting Local Decision Surfaces From Large Language Models

Ryan Chen, Youngmin Ko, Zeyu Zhang +5

We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated…

cs.CL2024★ 1 cited

AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model

Zeyu Zhang, Paul Groth, Iacer Calixto +1

Entity matching (EM) is the problem of determining whether two records refer to same real-world entity, which is crucial in data integration, e.g., for product catalogs or address…