4 papers
Exploiting the Randomness of Large Language Models (LLM) in Text Classification Tasks: Locating Privileged Documents in Legal Matters
Keith Huffman, Jianping Zhang, Nathaniel Huber-Fliflet +2
In legal matters, text classification models are most often used to filter through large datasets in search of documents that meet certain pre-selected criteria like relevance to a…
Leveraging Machine Learning and Large Language Models for Automated Image Clustering and Description in Legal Discovery
Qiang Mao, Fusheng Wei, Robert Neary +4
The rapid increase in digital image creation and retention presents substantial challenges during legal discovery, digital archive, and content management. Corporations and legal t…
A Comparative Study of Retrieval Methods in Azure AI Search
Qiang Mao, Han Qin, Robert Neary +4
Increasingly, attorneys are interested in moving beyond keyword and semantic search to improve the efficiency of how they find key information during a document review task. Large…
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
Fusheng Wei, Robert Neary, Han Qin +2
Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic…