EmoLLM: Multimodal Emotional Understanding Meets Large Language Models This paper introduces EmoBench, a comprehensive benchmark for evaluating the emotional understanding capabilities of multimodal large language models (MLLMs). EmoBench includes a diverse dataset of approximately 287,000 multimodal instructions and is designed to assess MLLMs across five emotional tasks. The paper also proposes EmoLLM, a novel model for multimodal emotional understanding that incorporates two core techniques: Multi-perspective Visual Projection and EmoPrompt. Experimental results show that EmoLLM significantly improves the performance of MLLMs in emotional understanding tasks, with an average improvement of 12.1% across multiple foundation models. EmoLLM is designed to enhance the ability of MLLMs to understand and reason about complex emotions in multimodal data. The model uses Multi-perspective Visual Projection to capture diverse emotional cues from visual data and EmoPrompt to guide the reasoning process. EmoPrompt leverages the capabilities of GPT-4V to generate accurate and contextually appropriate prompts, ensuring that the reasoning process is correct and leads to emotionally coherent conclusions. The paper also discusses the limitations of current MLLMs in emotional understanding, particularly in handling complex emotions such as anger and fear. The authors attribute this limitation to the lack of relevant data and specialized models. To address this issue, they introduce EmoBench, the first emotional instruction tuning dataset designed to enhance the emotional understanding capabilities of various MLLMs. The paper presents an extensive experimental evaluation of EmoLLM on the EmoBench benchmark, demonstrating its effectiveness in improving emotional understanding. The results show that EmoLLM outperforms baseline models in both close-set and open-set emotion classification tasks, as well as in three special emotional application tasks. The paper also discusses ablation studies that highlight the importance of the Multi-perspective Visual Projection and EmoPrompt techniques in enhancing the emotional understanding capabilities of MLLMs. The authors conclude that their work represents a significant step towards enabling MLLMs to achieve a deeper understanding of complex emotions in multimodal data, paving the way for emotionally intelligent AI systems. Future work could focus on addressing the limitations mentioned, such as increasing the diversity of EmoBench through a combination of automated and manual labeling, and mitigating the vulnerabilities of LLMs.EmoLLM: Multimodal Emotional Understanding Meets Large Language Models This paper introduces EmoBench, a comprehensive benchmark for evaluating the emotional understanding capabilities of multimodal large language models (MLLMs). EmoBench includes a diverse dataset of approximately 287,000 multimodal instructions and is designed to assess MLLMs across five emotional tasks. The paper also proposes EmoLLM, a novel model for multimodal emotional understanding that incorporates two core techniques: Multi-perspective Visual Projection and EmoPrompt. Experimental results show that EmoLLM significantly improves the performance of MLLMs in emotional understanding tasks, with an average improvement of 12.1% across multiple foundation models. EmoLLM is designed to enhance the ability of MLLMs to understand and reason about complex emotions in multimodal data. The model uses Multi-perspective Visual Projection to capture diverse emotional cues from visual data and EmoPrompt to guide the reasoning process. EmoPrompt leverages the capabilities of GPT-4V to generate accurate and contextually appropriate prompts, ensuring that the reasoning process is correct and leads to emotionally coherent conclusions. The paper also discusses the limitations of current MLLMs in emotional understanding, particularly in handling complex emotions such as anger and fear. The authors attribute this limitation to the lack of relevant data and specialized models. To address this issue, they introduce EmoBench, the first emotional instruction tuning dataset designed to enhance the emotional understanding capabilities of various MLLMs. The paper presents an extensive experimental evaluation of EmoLLM on the EmoBench benchmark, demonstrating its effectiveness in improving emotional understanding. The results show that EmoLLM outperforms baseline models in both close-set and open-set emotion classification tasks, as well as in three special emotional application tasks. The paper also discusses ablation studies that highlight the importance of the Multi-perspective Visual Projection and EmoPrompt techniques in enhancing the emotional understanding capabilities of MLLMs. The authors conclude that their work represents a significant step towards enabling MLLMs to achieve a deeper understanding of complex emotions in multimodal data, paving the way for emotionally intelligent AI systems. Future work could focus on addressing the limitations mentioned, such as increasing the diversity of EmoBench through a combination of automated and manual labeling, and mitigating the vulnerabilities of LLMs.