Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency

TL;DR

This paper thoroughly evaluates GPT-4o's language, vision, speech, and multimodal abilities using standardized tests, revealing high performance in many areas but also highlighting limitations in complex and ambiguous tasks.

Contribution

It provides a comprehensive benchmark of GPT-4o across multiple modalities, introducing new evaluation methods and highlighting areas for improvement.

Findings

GPT-4o excels in language and reasoning tasks with few-shot learning.

The model shows notable improvements in multimodal integration over predecessors.

Limitations remain in handling complex, ambiguous, and audio-visual inputs.

Abstract

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model's language capability. Additionally, GPT-4o's vision and speech capabilities are tested through image classification and object recognition tasks, as well as accent classification. The multimodal evaluation assesses the model's performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model's practical applicability and performance in real-world scenarios.