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An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software
arXiv – CS AI|Sina Gogani-Khiabani (University of Illinois Chicago), Ashutosh Trivedi (University of Colorado Boulder), Diptikalyan Saha (IBM Research), Saeid Tizpaz-Niari (University of Illinois Chicago)|
🤖AI Summary
Researchers developed a multi-agent LLM system that translates legal statutes into executable software, using U.S. tax preparation as a test case. The system achieved a 45% success rate using GPT-4o-mini, significantly outperforming larger frontier models like GPT-4o and Claude 3.5 which only achieved 9-15% success rates on complex tax code tasks.
Key Takeaways
- →A multi-agent LLM framework was developed to automatically translate legal statutes into working software code.
- →The system uses metamorphic testing to overcome the challenge of validating outputs when correct answers require legal interpretation.
- →GPT-4o-mini achieved 45% success rate while larger models like GPT-4o and Claude 3.5 only reached 9-15% on complex tax tasks.
- →The research demonstrates that smaller, specialized AI systems can outperform larger general-purpose models in domain-specific applications.
- →The approach shows promise for creating trustworthy AI systems for legally critical software applications.
Mentioned in AI
Models
GPT-4OpenAI
ClaudeAnthropic
#llm#legal-ai#multi-agent#metamorphic-testing#gpt-4o-mini#claude#tax-software#agentic-ai#code-generation#legal-tech
Read Original →via arXiv – CS AI
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