multimodal-in-context-learning

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papersTODAY 04:00 UTC

TwinICL Benchmark Tests Multimodal In-Context Learning With Paired Counterfactuals

Researchers released TwinICL, a procedurally generated benchmark that provides matched text and image versions of the same tasks, allowing direct comparison of in-context learning across modalities. The paired counterfactual design is intended to isolate how much a model's few-shot performance depends on the input format rather than the task itself. The work appears on arXiv under cs.LG.

benchmark/multimodal/in-context-learning/research

papersSEP 10 04:00 UTC

Paper proposes contrastive modeling to align reasoning paths in multimodal in-context learning

A newly released arXiv paper examines a weakness in how multimodal large language models use in-context learning, noting that current methods tend to copy superficial patterns from examples rather than the underlying reasoning process. The authors present a contrastive modeling technique designed to align a model's reasoning path with the logic demonstrated in-context. The approach is intended to improve performance across a range of multimodal tasks by fostering genuine reasoning instead of imitation.

multimodal/in-context-learning/reasoning/llms