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arXiv AI By Aaron Steiner, Ralph Peeters, Christian Bizer

Entity Matching using Decision Models

Read the original on arXiv AI →

Entity Matching using Decision Models explores a new class of large language model (LLM) approaches that output probabilities for predefined answer options instead of generating text. The study evaluates six decision models in a zero‑shot setting across eight benchmarks, comparing them to the generative LLM GPT‑6 Luna and the PLM‑based matcher Ditto. Results show that the top four decision models achieve mean F1 scores comparable to or slightly better than GPT‑6 Luna, outperform Ditto on most benchmarks, run faster and are cheaper, and can be further improved by a cascade that delegates uncertain pairs to an ensemble of generative LLMs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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