Summary of Measuring Agreeableness Bias in Multimodal Models, by Jaehyuk Lim et al.
Measuring Agreeableness Bias in Multimodal Models
by Jaehyuk Lim, Bruce W. Lee
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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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 A multimodal language study reveals that pre-marked options in question images can manipulate model responses, leading to a significant shift towards the pre-marked option. This phenomenon, known as the agreeableness bias, is found across various model architectures and could impact the reliability of these models in decision-making contexts where visual cues are present. The paper employs a systematic methodology to investigate this effect, presenting models with images of multiple-choice questions initially answered correctly, then exposing them to versions with pre-marked options. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that multimodal language models can be influenced by pre-marked options in question images, making their responses less reliable. Researchers found that when models were shown images with correct answers and then the same image with a different answer marked as correct, they tended to choose the marked option. This bias is seen across different model types and could affect how these models are used in real-world applications. |
