SecureGate reduces PII leakage up to 31.66X in federated LLM fine-tuning via token-gated dual LoRA adapters while preserving utility and achieving perfect routing reliability.
Neural Legal Judgment Prediction in English
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT's length limitation.
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UNVERDICTED 2representative citing papers
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.
citing papers explorer
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SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs
SecureGate reduces PII leakage up to 31.66X in federated LLM fine-tuning via token-gated dual LoRA adapters while preserving utility and achieving perfect routing reliability.
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In Context Learning and Reasoning for Symbolic Regression with Large Language Models
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.