Summary of Large Visual-language Models Are Also Good Classifiers: a Study Of In-context Multimodal Fake News Detection, by Ye Jiang and Yimin Wang
Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection
by Ye Jiang, Yimin Wang
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Large visual-language models (LVLMs) have demonstrated exceptional performance in various cross-modal benchmarks, but recent research suggests that Large Language Models (LLMs), such as GPT-3.5-turbo, underperform compared to well-trained smaller models like BERT in Fake News Detection (FND) tasks. To address this, the authors assess the FND capabilities of two notable LVLMs, CogVLM and GPT4V, alongside a smaller yet adeptly trained CLIP model in a zero-shot context, finding that LVLMs can achieve performance competitive with the smaller model. The paper then integrates standard in-context learning (ICL) with LVLMs, observing improvements in FND performance but limited in scope and consistency. To overcome this, the authors introduce the IMFND framework, enriching in-context examples and test inputs with predictions and corresponding probabilities from a well-trained smaller model. This strategic integration directs the LVLMs’ focus towards news segments associated with higher probabilities, enhancing their analytical accuracy. The experimental results demonstrate that the IMFND framework significantly boosts the FND efficiency of LVLMs, achieving enhanced accuracy over the standard ICL approach across three publicly available FND datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large visual-language models can analyze both images and text really well, but they don’t do as great a job detecting fake news. The authors wanted to see how these models would do if they were trained specifically for this task. They tested two big models, CogVLM and GPT4V, along with a smaller model called CLIP. Surprisingly, the big models did just as well as the small one! Next, they tried making the big models better at detecting fake news by giving them more information to work with. This helped a little bit, but not enough. So, the authors came up with a new way of using these models that involves giving them hints about what’s real and what’s fake. This made a huge difference! The results showed that this new method was much better at detecting fake news than just letting the big models figure it out on their own. |
Keywords
» Artificial intelligence » Bert » Gpt » Zero shot
