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    <abstract>Recent advancements in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability. Traditional Vision-Language models (VLMs) perform well in visual perception tasks while struggling with complex reasoning scenarios. Conversely, Large Language Models (LLMs) demonstrate robust text reasoning capabilities; however, they lack visual acuity. To bridge this gap, we propose **C**omplex **V**isual **R**easoning **L**arge **L**anguage **M**odels (**CVR-LLM**), capitalizing on VLMs’ visual perception proficiency and LLMs’ extensive reasoning capability. Unlike recent multimodal large language models (MLLMs) that require a projection layer, our approach transforms images into detailed, context-aware descriptions using an iterative self-refinement loop and leverages LLMs’ text knowledge for accurate predictions without extra training. We also introduce a novel multi-modal in-context learning (ICL) methodology to enhance LLMs’ contextual understanding and reasoning. Additionally, we introduce Chain-of-Comparison (CoC), a step-by-step comparison technique enabling contrasting various aspects of predictions. Our CVR-LLM presents the first comprehensive study across a wide array of complex visual reasoning tasks and achieves SOTA performance among all.</abstract>
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%0 Conference Proceedings
%T Enhancing Advanced Visual Reasoning Ability of Large Language Models
%A Li, Zhiyuan
%A Liu, Dongnan
%A Zhang, Chaoyi
%A Wang, Heng
%A Xue, Tengfei
%A Cai, Weidong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-enhancing-advanced
%X Recent advancements in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability. Traditional Vision-Language models (VLMs) perform well in visual perception tasks while struggling with complex reasoning scenarios. Conversely, Large Language Models (LLMs) demonstrate robust text reasoning capabilities; however, they lack visual acuity. To bridge this gap, we propose **C**omplex **V**isual **R**easoning **L**arge **L**anguage **M**odels (**CVR-LLM**), capitalizing on VLMs’ visual perception proficiency and LLMs’ extensive reasoning capability. Unlike recent multimodal large language models (MLLMs) that require a projection layer, our approach transforms images into detailed, context-aware descriptions using an iterative self-refinement loop and leverages LLMs’ text knowledge for accurate predictions without extra training. We also introduce a novel multi-modal in-context learning (ICL) methodology to enhance LLMs’ contextual understanding and reasoning. Additionally, we introduce Chain-of-Comparison (CoC), a step-by-step comparison technique enabling contrasting various aspects of predictions. Our CVR-LLM presents the first comprehensive study across a wide array of complex visual reasoning tasks and achieves SOTA performance among all.
%R 10.18653/v1/2024.emnlp-main.114
%U https://aclanthology.org/2024.emnlp-main.114/
%U https://doi.org/10.18653/v1/2024.emnlp-main.114
%P 1915-1929

Markdown (Informal)

[Enhancing Advanced Visual Reasoning Ability of Large Language Models](https://aclanthology.org/2024.emnlp-main.114/) (Li et al., EMNLP 2024)

• Enhancing Advanced Visual Reasoning Ability of Large Language Models (Li et al., EMNLP 2024)

ACL

• Zhiyuan Li, Dongnan Liu, Chaoyi Zhang, Heng Wang, Tengfei Xue, and Weidong Cai. 2024. Enhancing Advanced Visual Reasoning Ability of Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1915–1929, Miami, Florida, USA. Association for Computational Linguistics.