Summary of Enhancing Semantics in Multimodal Chain Of Thought Via Soft Negative Sampling, by Guangmin Zheng and Jin Wang and Xiaobing Zhou and Xuejie Zhang
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling
by Guangmin Zheng, Jin Wang, Xiaobing Zhou, Xuejie Zhang
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed SNSE-CoT method mitigates hallucinations in multimodal Chain of Thought (CoT) by generating soft negative rationales with high textual quality but illogical semantics. This approach addresses the issue of generated soft negative rationales not always improving answer accuracy due to hallucination. The study applies five methods for generating soft negative samples that share highly similar text but have different semantics from the original, and incorporates bidirectional margin loss (BML) into a traditional contrastive learning framework involving only positive and negative samples. Experimental results on the ScienceQA dataset demonstrate the effectiveness of SNSE-CoT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make Chain of Thought models work better is proposed in this study. These models are good at solving problems that require complex thinking, but they can sometimes produce wrong answers by guessing. To fix this problem, the researchers developed a method called SNSE-CoT that helps the model generate more accurate answers by introducing “negative” examples that are similar to the original question but make no sense. This approach was tested on a dataset of science questions and showed promising results. |
Keywords
» Artificial intelligence » Hallucination » Semantics
