Software Engineering Methods For AI-Driven Deductive Legal Reasoning
The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing.
Code (1)
Tasks
Formal LogicLegal ReasoningSimilar Papers 제목 키워드 기반
From Inductive to Deductive: LLMs-Based Qualitative Data Analysis in Requirements Engineering
Requirements Engineering (RE) is essential for developing complex and regulated software projects. Given the challenges in transforming stakeholder inputs into consistent software designs, Qualitative Data Analysis (QDA)…
Automatic deductive coding in discourse analysis: an application of large language models in learning analytics
Deductive coding is a common discourse analysis method widely used by learning science and learning analytics researchers for understanding teaching and learning interactions. It often requires researchers to manually la…
Feature EngineeringLanguage ModelingLanguage ModellingLarge Language Model+3MASLegalBench: Benchmarking Multi-Agent Systems in Deductive Legal Reasoning
Multi-agent systems (MAS), leveraging the remarkable capabilities of Large Language Models (LLMs), show great potential in addressing complex tasks. In this context, integrating MAS with legal tasks is a crucial step. Wh…
Legal ReasoningLegal Requirements Analysis
Modern software has been an integral part of everyday activities in many disciplines and application contexts. Introducing intelligent automation by leveraging artificial intelligence (AI) led to break-throughs in many f…
Closing the Loop: Formally Verified Law as a Reward Signal for Self-Improving Legal AI
This article develops an architecture that creates a formally verifiable reward signal to train legal AI, adapting the LLM proposes, verifier disposes paradigm from mathematical AI to the distinctive demands of law. We p…
Explanation Generation