Summary of Enhancing Court View Generation with Knowledge Injection and Guidance, by Ang Li et al.
Enhancing Court View Generation with Knowledge Injection and Guidance
by Ang Li, Yiquan Wu, Yifei Liu, Fei Wu, Ming Cai, Kun Kuang
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper presents a novel approach called Knowledge Injection and Guidance (KIG) for generating court views based on plaintiff claims and fact descriptions, a challenging task in Legal Artificial Intelligence (LegalAI). While Pretrained Language Models (PLMs) have shown success in natural language generation, they often struggle with the complex domain of CVG. KIG incorporates domain knowledge during training using a prompt encoder and dynamically guides text generation in the inference stage without altering the model’s architecture. This approach outperforms established baselines, especially in claim responsivity, by 11.87%. The paper demonstrates the effectiveness of KIG on real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Court View Generation (CVG) is a task that involves creating court views based on plaintiff claims and fact descriptions. While machines can do this job well with natural language generation tasks, they struggle when it comes to CVG because it’s a complex and knowledge-intensive process. This paper presents an approach called Knowledge Injection and Guidance (KIG) to help machines generate better court views. KIG uses special prompts during training to give the machine more information about the task at hand. It also helps guide the machine as it generates text, making sure it stays on track. The results show that KIG does a much better job than other approaches in this area. |
Keywords
» Artificial intelligence » Encoder » Inference » Prompt » Text generation
