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NTSEBENCH:面向视觉语言模型的认知推理基准

NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models

IIT Guwahati(印度古瓦哈蒂印度理工学院);University of Utah(犹他大学);University of Pennsylvania(宾夕法尼亚大学);Arizona State University(亚利桑那州立大学)

Pranshu Pandya, Vatsal Gupta, Agney S Talwarr, Tushar Kataria, Dan Roth, Vivek Gupta

AI总结:

本文提出NTSEBench多模态认知推理基准,包含2728道多选题和4642张图像,用于评估大型模型在复杂推理任务上的表现,并基于四种建模策略对比了开源与专有模型的基线性能。

摘要:

认知文本和视觉推理任务,包括谜题、序列和类比,要求具备快速推理、破译和评估文本与空间模式的能力。由于在海量人工整理数据上进行了广泛训练,LLMs和VLMs在常识推理任务中表现出色,但在需要更深认知理解的复杂推理方面仍显吃力。我们引入了NTSEBench,这是一个旨在评估大型模型认知多模态推理和解决问题技能的新数据集。该数据集包含2728道多项选择题,共附带4,642张图像,分为26种不同类型。这些问题取材于印度全国性的NTSE考试,混合了视觉和文本的一般能力挑战,旨在评估超越死记硬背的智力和批判性思维能力。我们使用最先进的LLMs和VLMs在该数据集上建立了基线。为了便于比较开源模型和专有模型,我们提出了四种不同的建模策略,以处理数据集实例中的不同模态——文本和图像。

英文摘要:

Cognitive textual and visual reasoning tasks, including puzzles, series, and analogies, demand the ability to quickly reason, decipher, and evaluate patterns both textually and spatially. Due to extensive training on vast amounts of human-curated data, LLMs and VLMs excel in common-sense reasoning tasks, however still struggle with more complex reasoning that demands deeper cognitive understanding. We introduce NTSEBench, a new dataset designed to evaluate cognitive multi-modal reasoning and problem-solving skills of large models. The dataset contains 2728 multiple-choice questions, accompanied by a total of 4,642 images, categorized into 26 different types. These questions are drawn from the nationwide NTSE examination in India and feature a mix of visual and textual general aptitude challenges, designed to assess intelligence and critical thinking skills beyond mere rote learning. We establish baselines on the dataset using state-of-the-art LLMs and VLMs. To facilitate a comparison between open source and propriety models, we propose four distinct modeling strategies to handle different modalities -- text and images -- in the dataset instances.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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