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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20384 |
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| _version_ | 1866909659937374208 |
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| author | Ivry, Dror Nahum, Oran |
| author_facet | Ivry, Dror Nahum, Oran |
| contents | This paper introduces two significant contributions to address the issue of grounding claims in a given context. Grounding means that given a context (document) and a claim, there's at least one supportive evidence for the claim in the document. We will introduce Paladin-mini, a compact (3.8B parameters) open-source classifier model (used for labeling data as grounded or ungrounded) engineered for robust performance in real-world scenarios, and the grounding-benchmark, a new evaluation dataset designed to assess performance on critical reasoning tasks. We'll also demonstrate the results of Paladin-mini with benchmarks against the current State-of-the-art and share clear and reproducible results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Paladin-mini: A Compact and Efficient Grounding Model Excelling in Real-World Scenarios Ivry, Dror Nahum, Oran Artificial Intelligence This paper introduces two significant contributions to address the issue of grounding claims in a given context. Grounding means that given a context (document) and a claim, there's at least one supportive evidence for the claim in the document. We will introduce Paladin-mini, a compact (3.8B parameters) open-source classifier model (used for labeling data as grounded or ungrounded) engineered for robust performance in real-world scenarios, and the grounding-benchmark, a new evaluation dataset designed to assess performance on critical reasoning tasks. We'll also demonstrate the results of Paladin-mini with benchmarks against the current State-of-the-art and share clear and reproducible results. |
| title | Paladin-mini: A Compact and Efficient Grounding Model Excelling in Real-World Scenarios |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20384 |