Paladin-mini: A Compact and Efficient Grounding Model Excelling in Real-World Scenarios

TL;DR

Paladin-mini is a compact, open-source grounding classifier with 3.8B parameters, designed for real-world scenarios, supported by a new evaluation dataset and benchmark comparisons.

Contribution

The paper introduces Paladin-mini, a small yet effective grounding model, and a new benchmarking dataset for assessing reasoning capabilities.

Findings

Paladin-mini achieves competitive performance on grounding tasks.

The grounding-benchmark effectively evaluates reasoning in real-world contexts.

Reproducible results demonstrate Paladin-mini's robustness.

Abstract

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.