Summary of On the Suitability Of Pre-trained Foundational Llms For Analysis in German Legal Education, by Lorenz Wendlinger et al.
On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education
by Lorenz Wendlinger, Christian Braun, Abdullah Al Zubaer, Simon Alexander Nonn, Sarah Großkopf, Christofer Fellicious, Michael Granitzer
First submitted to arxiv on: 20 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 research paper explores the capabilities of open-source foundational large language models (LLMs) in performing legal analysis tasks. The study finds that current LLMs can possess instruction capability and German legal background knowledge, making them suitable for some educational purposes. However, their performance deteriorates when faced with specific or complex tasks, such as classifying “Gutachtenstil” appraisal style components or analyzing complete legal opinions. To overcome these limitations, the authors propose a Retrieval Augmented Generation based prompt example selection method that significantly improves predictions in high data availability scenarios. The paper also evaluates the performance of pre-trained LLMs on two standard tasks for argument mining and automated essay scoring, finding it to be more adequate than previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research looks at how well artificial intelligence language models can help with legal work, like analyzing laws or writing essays. The study finds that these models are good at some things, but not great at others. For example, they’re okay at understanding basic law concepts, but struggle when it comes to more complex tasks. To make them better, the researchers came up with a new way of asking questions that helps the models make more accurate predictions. |
Keywords
» Artificial intelligence » Prompt » Retrieval augmented generation
