Summary of Understanding the Role Of Llms in Multimodal Evaluation Benchmarks, by Botian Jiang et al.
Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
by Botian Jiang, Lei Li, Xiaonan Li, Zhaowei Li, Xiachong Feng, Lingpeng Kong, Qi Liu, Xipeng Qiu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the role of Large Language Model (LLM) backbones in evaluating Multimodal Large Language Models (MLLMs). It identifies two critical aspects: whether current benchmarks truly assess multimodal reasoning and how LLM prior knowledge influences performance. The study introduces a modified evaluation protocol to disentangle LLM backbone contributions from multimodal integration, as well as an automatic knowledge identification technique to diagnose whether LLMs possess necessary knowledge for corresponding multimodal questions. The research encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs. Key findings reveal that some benchmarks allow high performance without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone, indicating a heavy reliance on language capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good large language models are at understanding pictures and words together. Right now, there are many ways to test these models, but it’s not clear which ones really show if they can think about things in a new way or just use the language part of their brain. The researchers did some special testing to figure out what’s going on and found that some tests don’t actually need pictures at all! They also found that many errors are because the models didn’t learn enough about the world. To fix this, they came up with a new way to make the models better by adding more knowledge. |
Keywords
» Artificial intelligence » Large language model
