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CALRK-Bench: Evaluating Context-Aware Legal Reasoning in Korean Law

JiHyeok Jung, TaeYoung Yoon, HyunSouk Cho

Abstract

Legal reasoning requires not only the application of legal rules but also an understanding of the context in which those rules operate. However, existing legal benchmarks primarily evaluate rule application under the assumption of fixed norms, and thus fail to capture situations where legal judgments shift or where multiple norms interact. In this work, we propose CALRK-Bench, a context-aware legal reasoning benchmark based on the legal system in Korean. CALRK-Bench evaluates whether models can identify the temporal validity of legal norms, determine whether sufficient legal information is available for a given case, and understand the reasons behind shifts in legal judgments. The dataset is constructed from legal precedents and legal consultation records, and is validated by legal experts. Experimental results show that even recent large language models consistently exhibit low performance on these three tasks. CALRK-Bench provides a new stress test for evaluating context-aware legal reasoning rather than simple memorization of legal knowledge. Our code is available at https://github.com/jhCOR/CALRKBench.

CALRK-Bench: Evaluating Context-Aware Legal Reasoning in Korean Law

Abstract

Legal reasoning requires not only the application of legal rules but also an understanding of the context in which those rules operate. However, existing legal benchmarks primarily evaluate rule application under the assumption of fixed norms, and thus fail to capture situations where legal judgments shift or where multiple norms interact. In this work, we propose CALRK-Bench, a context-aware legal reasoning benchmark based on the legal system in Korean. CALRK-Bench evaluates whether models can identify the temporal validity of legal norms, determine whether sufficient legal information is available for a given case, and understand the reasons behind shifts in legal judgments. The dataset is constructed from legal precedents and legal consultation records, and is validated by legal experts. Experimental results show that even recent large language models consistently exhibit low performance on these three tasks. CALRK-Bench provides a new stress test for evaluating context-aware legal reasoning rather than simple memorization of legal knowledge. Our code is available at https://github.com/jhCOR/CALRKBench.

Paper Structure

This paper contains 45 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. General Legal Benchmarks
  4. Temporal Context Benchmarks
  5. Sufficient Context Benchmarks
  6. Shift Context Benchmarks
  7. Context-Aware Legal Reasoning Benchmark
  8. Task Formulation
  9. Expert Validation
  10. Dataset Statistics
  11. Experiments
  12. Experimental Settings
  13. Base dataset
  14. Models and evaluation metric
  15. Overall Performance
  16. ...and 30 more sections

Figures (3)

  • Figure 1: Overview of the Benchmark.
  • Figure 2: Row-normalized confusion matrices for Type-JSA. Each value represents the percentage of predictions within the same gold label.
  • Figure 3: For the experiments involving context addition, the LexEval data were randomly sampled or the strings were randomly generated. Results averaged over three runs.