arXiv:2609.18908v1 Announce Type: new Abstract: Existing legal benchmarks cover diverse tasks, while continuous monetary remedies remain comparatively underexplored. We introduce ECtHR-NPD, to the be...
arXiv:2608. 10268v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate legal determinations over what human rights are realized, and how.
arXiv:2605. 28183v2 Announce Type: replace-cross Abstract: We introduce the BenGER (Benchmark for German Law) dataset for evaluating LLM systems on subsumption-based legal reasoning in German law.
arXiv:2509.00141v2 Announce Type: replace-cross Abstract: Statutory corpora and judicial decisions are growing faster than legal professionals can read them, while individual judgments often exceed t...
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.
The paper introduces TIR-EC, a tool‑integrated reinforcement learning framework designed to improve large language models (LLMs) for emergency department prediction tasks. TIR-EC exposes groups of electronic health record (EHR) features as callable tools and trains the LLM using rewards from a gradient‑boosted tree teacher, while a reverse curriculum gradually removes prompt‑based skills to embed task‑specific clinical knowledge into the model weights. Experiments on three clinical prediction tasks show that a 4B TIR‑EC agent achieves the highest macro F1 among compared models, surpasses conventional machine‑learning baselines, and competes with much larger frontier LLMs, while also selecting more useful evidence with fewer tool calls.
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.
arXiv:2609.22529v1 Announce Type: new Abstract: International law provides the normative framework through which states coordinate action, regulate armed conflict, and protect human rights, yet its t...
arXiv:2610.15179v1 Announce Type: cross Abstract: Large language models are increasingly deployed in contexts requiring adherence to ethical and legal norms, yet their alignment with internationally...
arXiv:2503.00128v2 Announce Type: replace Abstract: Language models offer a way to scale costly, labour-intensive expert annotation, but their ability to reproduce expert judgements remains uncertain...
arXiv:2606. 23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights.
arXiv:2610.07132v1 Announce Type: cross Abstract: Croissant has emerged as a standard for machine-readable dataset metadata, yet populating its fields remains labor-intensive and requires careful rea...