Summary of A Multi-source Heterogeneous Knowledge Injected Prompt Learning Method For Legal Charge Prediction, by Jingyun Sun et al.
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction
by Jingyun Sun, Chi Wei, Yang Li
First submitted to arxiv on: 5 Aug 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 prompt learning framework-based method for predicting legal charges by leveraging multi-source external knowledge from a legal knowledge base, a conversational language model, and related legal articles. The approach matches knowledge snippets in case descriptions with the legal knowledge base and encapsulates them into the input through a hard prompt template. Additionally, it retrieves legal articles related to a given case description through contrastive learning and obtains factual elements within the case description using a conversational LLM. The method fuses the embedding vectors of soft prompt tokens with the encoding vector of factual elements to achieve knowledge-enhanced model forward inference. Experimental results show that this approach achieves state-of-the-art results on the CAIL-2018 dataset, which is the largest legal charge prediction dataset, and has lower data dependency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict what kind of charge someone might face in a court case by using lots of different sources of information. It’s like looking up things in a big library or talking to a smart AI assistant to figure out what’s important about the case. The researchers used this approach and tested it on a really big dataset, and it did better than other methods that didn’t use as much information. This is important because it helps us understand why charges are given and how we can make predictions more accurately. |
Keywords
» Artificial intelligence » Embedding » Inference » Knowledge base » Language model » Prompt
