NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Organizations: Department of Computer Science, AI Institute, University of South Carolina, Columbia, South Carolina, USA · Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA · Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA
Abstract
Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications. While LLMs show promise for legal text analysis and generation, they struggle with accurate citation attribution and precedent verification. For example, in legal contexts, a single incorrect precedent can jeopardize a case. Current approaches to improve LLM reliability in legal domains suffer from two key limitations: inadequate integration of structured legal knowledge during training or fine-tuning, and insufficient verification mechanisms for generated legal content. To address these challenges, we propose the TRISM (Trustworthy, Reliable, Interpretable, Safe Models) framework, which integrates NeuroSymbolic AI principles with LLMs to leverage both neural learning capabilities and symbolic reasoning over structured legal knowledge. The TRISM approach addresses the above limitations while maintaining interpretable decision pathways. Our framework formalizes the extraction of symbolic knowledge from legal textual documents and incorporates Retrieval-Augmented Generation (RAG) as a core component for grounding LLM outputs in verified legal sources. In this position paper, we make the following contributions: (1) An analysis of the limitations of AI in law; (2) Introduce RASOR RAG which creates foundations for neurosymbolic RAG by generating explicit interpretable rationales that could be formalized into symbolic representations; (3) A formalized methodology for creating symbolic legal knowledge bases that support both interpretable reasoning and output verification in LLMs; and (4) The TRISM framework for integrating symbolic legal knowledge with LLMs.
Figures & tables
| Task | Category | Knowledge Encoding | Reference |
| KG Construction | Construction | – | Kondo et al. (2024) , Hlyzov et al. (2017) , Filtz (2017) , Tang et al. (2020) , Song and de la Clergerie (2020) |
| Charge/Judgement Prediction | Classification | Explicit | Bi et al. (2024) , Gan et al. (2021) |
| Implict | He et al. (2023) , Wang et al. (2024b) | ||
| Legal Statute Identification | Explicit | Nguyen et al. (2023) | |
| Question Answer | Question Answer | Explicit | Jiang et al. (2024) |
| Implicit | Louis et al. (2024) , McElvain et al. (2019) |