Summary of Automatic Benchmarking Of Large Multimodal Models Via Iterative Experiment Programming, by Alessandro Conti et al.
Automatic benchmarking of large multimodal models via iterative experiment programming
by Alessandro Conti, Enrico Fini, Paolo Rota, Yiming Wang, Massimiliano Mancini, Elisa Ricci
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 In this paper, researchers present a novel framework called APEx that automates the process of benchmarking large multimodal models (LMMs). The framework utilizes a large language model (LLM) to generate a set of experiments based on a research question expressed in natural language. APEx then compiles a scientific report, which guides the testing procedure and refines the results using the LLM. This modular approach allows for flexibility and extensibility as new tools become available. The authors demonstrate the effectiveness of APEx by reproducing existing studies while enabling arbitrary analyses and hypothesis testing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large multimodal models (LMMs) are becoming increasingly important in many areas, but evaluating their capabilities can be time-consuming and costly. Researchers have developed a way to automate this process using a framework called APEx. It starts with a question about what the model is good at, then uses a large language model to create experiments and write a report about the results. The report helps decide which experiments to do next and whether the findings are enough to make conclusions. This makes it easier and faster to evaluate LMMs. |
Keywords
» Artificial intelligence » Large language model
