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LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence
🤖AI Summary
Researchers developed LexChronos, an AI framework that extracts structured event timelines from Indian Supreme Court judgments using a dual-agent architecture. The system achieved 0.8751 F1 score on synthetic data and showed 75% preference over unstructured approaches in legal text summarization tasks.
Key Takeaways
- →LexChronos uses a dual-agent AI architecture with LoRA-instruct-tuned extraction and feedback agents to process legal documents.
- →The framework addresses the lack of Indian legal datasets by creating a synthetic corpus of 2000 samples using DeepSeek-R1 and GPT-4.
- →The system achieved a BERT-based F1 score of 0.8751 against synthetic ground truth data.
- →GPT-4 preferred structured timelines over unstructured baselines in 75% of legal text summarization cases.
- →The framework enables future legal AI applications including precedent mapping, argument synthesis, and predictive judgment modeling.
#ai#legal-tech#llm#natural-language-processing#indian-law#structured-data#machine-learning#judicial-analysis
Read Original →via arXiv – CS AI
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