mlsanityData to 7 Oct 2026

Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery

arXiv 2405.19164 ↗First posted 29 May 2024cs.AI cs.IRcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Electronic Discovery (eDiscovery) requires identifying relevant documents from vast collections for legal production requests. While artificial intelligence (AI) and natural language processing (NLP) have improved document review efficiency, current methods still struggle with legal entities, citations, and complex legal artifacts. To address these challenges, we introduce DISCOvery Graph (DISCOG), an emerging system that integrates knowledge graphs for enhanced document ranking and classification, augmented by LLM-driven reasoning. DISCOG outperforms strong baselines in F1-score, precision, and recall across both balanced and imbalanced datasets. In real-world deployments, it has reduced litigation-related document review costs by approximately 98%, demonstrating significant business impact.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 19 of its 59 references that are in the map. First posted on arXiv 29 May 2024.

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19 of 59 references.

References · 19

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7 more
  1. 2024Can Large Language Models Identify Authorship?cited by 40
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