Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery
<|reference_start|>Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery: Electronic Discovery (eDiscovery) involves identifying relevant documents from a vast collection based on legal production requests. The integration of artificial intelligence (AI) and natural language processing (NLP) has transformed this process, helping document review and enhance efficiency and cost-effectiveness. Although traditional approaches like BM25 or fine-tuned pre-trained models are common in eDiscovery, they face performance, computational, and interpretability challenges. In contrast, Large Language Model (LLM)-based methods prioritize interpretability but sacrifice performance and throughput. This paper introduces DISCOvery Graph (DISCOG), a hybrid approach that combines the strengths of two worlds: a heterogeneous graph-based method for accurate document relevance prediction and subsequent LLM-driven approach for reasoning. Graph representational learning generates embeddings and predicts links, ranking the corpus for a given request, and the LLMs provide reasoning for document relevance. Our approach handles datasets with balanced and imbalanced distributions, outperforming baselines in F1-score, precision, and recall by an average of 12%, 3%, and 16%, respectively. In an enterprise context, our approach drastically reduces document review costs by 99.9% compared to manual processes and by 95% compared to LLM-based classification methods<|reference_end|>
@article{lahiri2024learning,
    title={Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in
  eDiscovery},
    author={Sounak Lahiri, Sumit Pai, Tim Weninger, Sanmitra Bhattacharya},
    journal={arXiv preprint arXiv:2405.19164},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19164},
    primaryClass={cs.AI cs.IR}
}