Summary of Genception: Evaluate Vision Llms with Unlabeled Unimodal Data, by Lele Cao et al.
GenCeption: Evaluate Vision LLMs with Unlabeled Unimodal Data
by Lele Cao, Valentin Buchner, Zineb Senane, Fangkai Yang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to evaluating Multimodal Large Language Models (MLLMs) called GenCeption, which eliminates the need for expensive annotated multimodal data. The method assesses MLLM’s inter-modality semantic coherence and tendency to hallucinate using unimodal data, making it a more efficient and annotation-free evaluation process. Inspired by the DrawCeption game, GenCeption involves iterative description and generation steps, with the semantic drift across iterations quantified using the GC@T metric. The paper focuses on applying this method to Vision LLMs (VLLMs) and establishes the MMECeption benchmark for evaluating VLLM performance. By comparing popular VLLMs and human annotators, the study validates GenCeption’s effectiveness in predicting established VLLM benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GenCeption is a new way to test artificial intelligence models that can understand different types of information like images, text, or sound. Normally, these models are tested using lots of labeled data, which is expensive and time-consuming. GenCeption creates its own tests without needing any labels, making it faster and more efficient. It works by having a computer describe an image and then generate more text about the same thing. The study shows that this method can accurately predict how well these models will perform on other tasks. |
