arXiv AI By Amogh Raina, Ilias Chalkidis, Daniel Hershcovich, Henrik Palmer Olsen

Can LLMs Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases

Read the original on arXiv AI →

The study examines whether large language models (LLMs) can perform legally meaningful reasoning by testing OpenAI GPT 5.4 on European Court of Human Rights case forecasting. Using various prompting strategies, the authors find that the model produces structurally complete but substantively shallow analyses, and that LLM-as-a-Judge evaluators are internally consistent yet only weakly aligned with human annotators. The expert-curated prompt yields more comprehensive reasoning but does not improve prediction accuracy, leading the authors to caution against relying solely on automated LLM evaluation or using task accuracy as a proxy for reasoning quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Aug 18

When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning

arXiv:2608. 14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capability we term temporal applicable-law determination.

By Yiqian Huang, Shuyuan Zheng, Qianying Liu, Shaowen Peng, Yuntao Kong, Kotaro Funakoshi, Chuan Xiao, Manabu Okumura, Yang Cao
arXiv AI
Sep 3

OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction

The paper introduces OBJECTION, an inference-time pipeline that adds an Adversarial Lawyer Agent to each of the three reasoning steps—offense, unlawfulness, and culpability—in legal judgment prediction models. By actively injecting defense arguments, the agent challenges the model’s default assumption of guilt, which is common in datasets biased toward guilty outcomes. Using a new Natural Innocent dataset of 3.4k real cases, OBJECTION reduces the False Guilty Rate from 82.93% to 16.69%, demonstrating significant improvement in substantive legal reasoning.

By Jaehoon Jeong, Jay-Yoon Lee
arXiv AI
Aug 6

Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools

arXiv:2604. 26233v3 Announce Type: replace Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range of judicial and administrative contexts, it becomes essential to explore how they answer legal questions, and in particular the factors that lead them to decide difficult questions.

By Oisin Suttle, David Lillis