Prompt-tuning with Attribute Guidance for Low-resource Entity Matching
Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or Undecidable. Traditional EM approaches rely heavily on supervised learning, which requires large amounts of high-quality labeled data. This labeling process is both time-consuming and costly, limiting practical applicability. As a result, there is a strong need for low-resource EM methods that can perform well with minimal labeled data. Recent prompt-tuning approaches have shown promise for low-resource EM, but they mainly focus on entity-level matching and often overlook critical attribute-level information. In addition, these methods typically lack interpr...
| Authors | Lihui Liu, Carl Yang |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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