Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark

• URL: http://arxiv.org/abs/2311.13053v1
• Date: Tue, 21 Nov 2023 23:26:05 GMT
• Title: Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark
• Authors: Xiao Liu, Jianfeng Lin, Jiawei Zhang
• Abstract summary: This study introduces MultiAPI, a pioneering comprehensive large-scale API benchmark dataset. It consists of 235 diverse API calls and 2,038 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation.
• Score: 11.572835837392867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these models predominantly excel in text-based tasks, overlooking the complexity of real-world multimodal information. This study introduces MultiAPI, a pioneering comprehensive large-scale API benchmark dataset aimed at expanding LLMs' proficiency in multimodal contexts. Developed collaboratively through ChatGPT, MultiAPI consists of 235 diverse API calls and 2,038 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Through comprehensive experiments, our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation. What's more, we surprisingly notice that auxiliary context can actually impair the performance. An in-depth error analysis paves the way for a new paradigm to address these challenges, suggesting a potential direction for future LLM research.

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