aiqbench analysis
The paper introduces a benchmark that splits 168 tasks into General, Adaptability, and Challenge categories, so readers can see whether gains come from broad capability, task-specific tuning, or robustness. Its main finding is that adding training data helps adaptation without hurting general performance, but Challenge scores stay below 5.0, with multilingual and visual-document tasks as the clearest weak spots. For practitioners, the result suggests that more data alone will not close these gaps and that the field needs methodological advances.
TRACE: Training-aware Robustness and Challenge Evaluation for Text-Vision Embedding Models
arXiv:2610.26993v1 Announce Type: new Abstract: Existing benchmarks for text-vision embedding models report aggregate scores that obscure whether performance comes from general capabilities, task-specific adaptation, or robustness to challenging tasks. We introduce TRACE, a Training-aware Robustness and Challenge Evaluation framework that reorganizes 168 tasks from various sources of datasets into three categories: General, Adaptability, and Challenge. Through controlled experiments on two text-vision embedding models, we show that combining general and adaptation data improves Adaptability performance by 7.67 and 7.09 points, respectively, while largely preserving General performance. However, Challenge performance remains below 5.0, with limited improvements from additional training data. Further analysis identifies multilingual and visual-document tasks as major weaknesses. Our findings demonstrate that data adaptation alone is insufficient to address these challenges, highlighting the need for methodological advances beyond data-centric improvements.
Abstract:Existing benchmarks for text-vision embedding models report aggregate scores that obscure whether performance comes from general capabilities, task-specific adaptation, or robustness to challenging tasks. We introduce TRACE, a Training-aware Robustness and Challenge Evaluation framework that reorganizes 168 tasks from various sources of datasets into three categories: General, Adaptability, and Challenge. Through controlled experiments on two text-vision embedding models, we show that combining general and adaptation data improves Adaptability performance by 7.67 and 7.09 points, respectively, while largely preserving General performance. However, Challenge performance remains below 5.0, with limited improvements from additional training data. Further analysis identifies multilingual and visual-document tasks as major weaknesses. Our findings demonstrate that data adaptation alone is insufficient to address these challenges, highlighting the need for methodological advances beyond data-centric improvements.
Computation and Language (cs.CL)
arXiv:2610.26993 [cs.CL]
(or arXiv:2610.26993v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.26993 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 218 words