Summary of Tp-eval: Tap Multimodal Llms’ Potential in Evaluation by Customizing Prompts, By Yuxuan Xie et al.
TP-Eval: Tap Multimodal LLMs’ Potential in Evaluation by Customizing Prompts
by Yuxuan Xie, Tianhua Li, Wenqi Shao, Kaipeng Zhang
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The paper addresses the issue of prompt sensitivity in evaluating multimodal large language models (MLLMs), which is crucial for understanding their attributes and performance. Current benchmarks overlook this problem, leading to significant fluctuations in model performance due to minor prompt variations. The proposed TP-Eval framework introduces a prompt customization method to reduce evaluation biases and tap into each model’s potential. This is achieved by rewriting original prompts into customized ones tailored to individual models. Extensive experiments demonstrate the effectiveness of TP-Eval in uncovering MLLM capabilities, making it a valuable contribution to developing comprehensive and convincing evaluation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we can make sure that when we test big language models, we’re not tricked into thinking they’re better or worse than they really are. Right now, the way we test these models is flawed because small changes in what we ask them to do can make a big difference in how well they do. To fix this, the paper proposes a new way of testing called TP-Eval that makes sure each model gets asked different questions. This helps us see which models are really good and which ones need more work. |
Keywords
» Artificial intelligence » Prompt
