Summary of Interpreting and Editing Vision-language Representations to Mitigate Hallucinations, by Nick Jiang et al.
Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations
by Nick Jiang, Anish Kachinthaya, Suzie Petryk, Yossi Gandelsman
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A study investigates internal image representations of vision-language models (VLMs) to address hallucinations, a persistent challenge despite advances in model size and training. The researchers project VLMs’ internal image representations onto their language vocabulary and observe more confident output probabilities on real objects than hallucinated objects. They also use these output probabilities to spatially localize real objects. Building on this approach, the authors introduce a knowledge erasure algorithm that removes hallucinations by linearly orthogonalizing image features with respect to hallucinated object features. The study shows that targeted edits to a model’s latent representations can reduce hallucinations by up to 25.7% on the COCO2014 dataset while preserving performance. This work demonstrates how a deeper understanding of VLMs’ latent representations can enhance reliability and enable novel capabilities, such as zero-shot segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers studied vision-language models (VLMs) to figure out why they sometimes make mistakes by showing images that aren’t really there. They found that when they looked at the internal workings of these models, they could tell which images were real and which weren’t. This helped them develop a new way to make the models less likely to make those kinds of mistakes. The researchers also showed that this new approach didn’t harm the models’ ability to recognize things correctly. Overall, this study shows how understanding more about VLMs can help make them better at recognizing what’s real and what’s not. |
Keywords
» Artificial intelligence » Zero shot
