Using synthetic formal languages and a new discriminative evaluation metric, the paper shows that fine-tuning outperforms in-context learning on in-distribution language generalization but both perform equally on out-of-distribution generalization across 18 LLMs.
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Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search
Using synthetic formal languages and a new discriminative evaluation metric, the paper shows that fine-tuning outperforms in-context learning on in-distribution language generalization but both perform equally on out-of-distribution generalization across 18 LLMs.