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arXiv AI

Entity Matching using Decision Models

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.

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DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

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Hugging Face Trending Papers
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Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison

The paper reproduces five factuality alignment methods for large language models—three on‑policy RL approaches (PPO, GRPO, Dr.GRPO) and two offline preference optimization methods (DPO, KTO)—using a common benchmark and evaluation metrics. None of the methods consistently outperforms the base model, and the rewards appear to favor format, length, or brevity rather than factuality or relevance. The study also highlights problems with current evaluation practices, noting that metrics and benchmarks often fail to measure prompt relevance and rely on fragile LLM judges and retrieval for verification.

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