Summary of Mitigating Multilingual Hallucination in Large Vision-language Models, by Xiaoye Qu et al.
Mitigating Multilingual Hallucination in Large Vision-Language Models
by Xiaoye Qu, Mingyang Song, Wei Wei, Jianfeng Dong, Yu Cheng
First submitted to arxiv on: 1 Aug 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 hallucination problem in Large Vision-Language Models (LVLMs) when querying images in non-English languages. While existing methods focus on English scenarios, this study proposes a two-stage Multilingual Hallucination Removal (MHR) framework to mitigate hallucinations for both high-resource and low-resource languages. The MHR framework leverages the LVLM’s inherent capabilities and employs a novel cross-lingual alignment method to generate multiple responses and identify hallucination-aware pairs for each language. Experimental results show that the proposed framework achieves a substantial reduction in hallucination generation, with an average increase of 19.0% in accuracy across 13 different languages on the extended multilingual POPE benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about fixing a big problem in computers that can see and understand words from many languages. These computers are really good at doing lots of things, but sometimes they make up answers that aren’t true. This happens especially when they’re asked to look at pictures from languages they don’t know very well. The researchers came up with a new way to help these computers be more accurate by using the computers’ own abilities and some special tricks. They tested this new method on many different languages and found it worked really well, making the computers better at giving true answers. |
Keywords
* Artificial intelligence * Alignment * Hallucination
