arXiv cs.AI· Gordon V. Cormack, Maura R. Grossman, Jeremy Pickens·· 3 天前AI 评分22
比较 GenAI、机器学习与人工审阅在 eDiscovery 中的效果
Comparing GenAI to Machine Learning and Manual Review for eDiscovery
AI 导读
针对 Redgrave LLP 案例研究中 Relativity 生成式 AI 审阅工具 aiR 达 88% 召回率、其机器学习工具 RAL 仅 64% 召回率的结论，分析指出两者方法目标不同、评估者间存在分歧，结果不可直接比较。
正文 · 原文
Abstract:Keeling et al. [Keeling2026Generative] report on a case study recently conducted by Redgrave LLP in which they assert that Relativity's Generative AI Review tool (aiR) achieved 88% recall while Relativity Assisted Learning (RAL), its machine-learning tool, achieved 64% recall. These results are not comparable because the methods employed have different objectives, because of inter-assessor disagreement, and because they represent different recall-precision and cost-benefit trade-offs. Our analysis of the Redgrave data indicates that, when these differences are taken into account, aiR and manual review are comparable. Furthermore, established machine-learning models employing Continuous Active Learning (CAL) show state-of-the-art effectiveness, and superior efficiency, when trained only on a sample of 2,000 first-pass-reviewer responsiveness determinations.
| Comments: | 8 pages; 2 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.16925 [cs.LG] |
| (or arXiv:2610.16925v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16925 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gordon Cormack [view email]
[v1]
Mon, 12 Oct 2026 15:08:40 UTC (63 KB)
来源：arXiv cs.AI · arxiv.org