Summary of Attention with Dependency Parsing Augmentation For Fine-grained Attribution, by Qiang Ding et al.
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution
by Qiang Ding, Lvzhou Luo, Yixuan Cao, Ping Luo
First submitted to arxiv on: 16 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 This paper proposes a fine-grained attribution mechanism for validating RAG-generated content, enabling humans to efficiently verify answers with supporting evidence from retrieved documents. The existing methods rely on model-internal similarity metrics, such as saliency scores and hidden state similarity, but suffer from high computational complexity or coarse-grained representations. To address these limitations, the authors introduce two techniques applicable to all model-internals-based methods. Firstly, they aggregate token-wise evidence through set union operations, preserving the granularity of representations. Secondly, they enhance the attributor by integrating dependency parsing to enrich the semantic completeness of target spans. The proposed method employs attention weights as the similarity metric and outperforms prior works in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers generate answers that can be easily checked by humans. Right now, it’s hard for people to verify the accuracy of these generated answers because they don’t have enough information about how the computer arrived at its answer. The authors suggest two new ways to improve this process. First, they group small pieces of evidence together to make it easier to see why the computer chose a particular answer. Second, they use a technique called dependency parsing to add more context to the answers. This makes it easier for people to understand how the computer arrived at its answer. The results show that these new methods work better than existing ones. |
Keywords
» Artificial intelligence » Attention » Dependency parsing » Rag » Token
