Summary of An Entailment Tree Generation Approach For Multimodal Multi-hop Question Answering with Mixture-of-experts and Iterative Feedback Mechanism, by Qing Zhang et al.
An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanism
by Qing Zhang, Haocheng Lv, Jie Liu, Zhiyun Chen, Jianyong Duan, Hao Wang, Li He, Mingying Xv
First submitted to arxiv on: 8 Dec 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 unified approach for multimodal multi-hop question answering tackles two key challenges: redundant information and lack of interpretable reasoning steps. The method treats this problem as a joint entailment tree generation and question answering task, utilizing a multi-task learning framework with mixture-of-experts to prevent errors from interfering. An iterative feedback mechanism refines the potential answer by feeding back results to the Large Language Model (LLM) for regenerating entailment trees. This approach achieves competitive results on MultimodalQA and wins the first place in the official WebQA leaderboard since April 10, 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to answer complex questions that involve multiple pieces of information from different sources. The problem is that current methods often get confused by too much irrelevant information, which makes it harder for them to make good decisions. To solve this, the authors suggest a two-part approach: first, generate a tree-like structure that shows how all the relevant information relates to each other, and then use this structure to answer the question. The authors test their method on several datasets and show that it performs well compared to existing methods. |
Keywords
» Artificial intelligence » Large language model » Mixture of experts » Multi task » Question answering
