LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues

• URL: http://arxiv.org/abs/2604.19464v1
• Date: Tue, 21 Apr 2026 13:42:24 GMT
• Title: LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues
• Abstract summary: More than half of the global population struggles to meet their civil justice needs due to limited legal resources.<n>We propose LePREC, a neuro-symbolic framework combining neural generation with structured statistical reasoning.
• Score: 21.269183638841195
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, significant challenges remain even at the foundational step of legal issue identification. To investigate LLMs' capabilities in this task, we constructed a dataset from 769 real-world Malaysian Contract Act court cases, using GPT-4o to extract facts and generate candidate legal issues, annotated by senior legal experts, which reveals a critical limitation: while LLMs generate diverse issue candidates, their precision remains inadequate (GPT-4o achieves only 62%). To address this gap, we propose LePREC (Legal Professional-inspired Reasoning Elicitation and Classification), a neuro-symbolic framework combining neural generation with structured statistical reasoning. LePREC consists of: (1) a neuro component leverages LLMs to transform legal descriptions into question-answer pairs representing diverse analytical factors, and (2) a symbolic component applies sparse linear models over these discrete features, learning explicit algebraic weights that identify the most informative reasoning factors. Unlike end-to-end neural approaches, LePREC achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. Experiments show a 30-40% improvement over advanced LLM baselines, including GPT-4o and Claude, confirming that correlation-based factor-issue analysis offers a more data-efficient solution for relevance decisions.

Related papers

• LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction [4.655574344274635]
  We propose an enhanced causal inference framework that integrates Large Language Model priors with statistical causal discovery.<n>Our proposed method significantly outperforms state-of-the-art baselines in both predictive accuracy and robustness.
  arXiv  Detail & Related papers  (2026-03-12T02:14:16Z)
• Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective [53.594353527056775]
  We propose Chinese Commonsense Multi-hop Reasoning ( CCMOR) to evaluate Large Language Models (LLMs)<n> CCMOR is designed to evaluate LLMs' ability to integrate Chinese-specific factual knowledge with multi-step logical reasoning.<n>We implement a human-in-the-loop verification system, where domain experts systematically validate and refine the generated questions.
  arXiv  Detail & Related papers  (2025-10-09T20:29:00Z)
• Benchmarking LLM Causal Reasoning with Scientifically Validated Relationships [21.03106821967686]
  Causal reasoning is fundamental for Large Language Models to understand genuine cause-and-effect relationships.<n>Existing benchmarks suffer from critical limitations such as reliance on synthetic data and narrow domain coverage.<n>We introduce a novel benchmark constructed from casually identified relationships extracted from top-tier economics and finance journals.
  arXiv  Detail & Related papers  (2025-10-08T17:00:49Z)
• Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLM [42.11889345473452]
  Legal Article Prediction (LAP) is a critical task in legal text classification.<n>We propose Uni-LAP, a universal framework for legal article prediction.
  arXiv  Detail & Related papers  (2025-09-26T09:42:20Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• Leveraging Prompts in LLMs to Overcome Imbalances in Complex Educational Text Data [1.8280573037181356]
  We explore the potential of Large Language Models (LLMs) with assertions to mitigate imbalances in educational datasets. This issue is especially prominent in the education sector, where cognitive engagement levels among students show significant variation in their open responses.
  arXiv  Detail & Related papers  (2024-04-28T00:24:08Z)
• CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs [27.362012903540492]
  The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning. The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-04-09T14:40:08Z)
• Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
  This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces. We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered. Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
  arXiv  Detail & Related papers  (2023-12-04T18:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.