Text classification is a well-established area of machine learning that involves automatically categorizing text into predefined categories, such as positive or negative in sentiment analysis. It typically involves applying a machine learning algorithm to learn a predictive model from a set of labeled training texts and using the model to classify new texts. Large language models (LLMs) have been successfully applied to various natural language processing tasks, including text classification. There are two main approaches to text classification with LLMs: In-Context Learning and Fine-Tuning. In-Context Learning involves prompt engineering, allowing the model to learn a new task using only a few demonstration examples, while Fine-Tuning adjusts the model’s parameters with additional labeled data. Retrieval-Augmented Generation (RAG) is a retrieval process that enhances LLM performance by selecting relevant examples. This paper presents our work on utilizing In-Context Learning and RAG to identify delay-related statements during the document review process of construction delay disputes. We also report the results of our experiments comparing the accuracy of In-Context Learning with that of traditional machine learning algorithms, such as logistic regression and KNN.

The costs involved in manually reviewing documents in legal civil litigations have grown dramatically as more and more information is stored electronically. As a result, the document review process can require an extraordinary dedication of resources. In construction litigations, quickly finding supporting documentation in a delay dispute is critical to the success of a matter. Identifying relevant delay-related communications and supporting documentation has historically been expensive and time consuming. Using machine learning technologies, respondents can be more comprehensive in their assessment of the data requiring review to respond to the claim in time. Explainable machine learning is an active machine learning research area, and in an explainable machine learning system, predictions generated from a machine learning model are explainable and human understandable. In delay dispute ‘document review’ scenarios, a document can be identified as delay-related, as long as one or more of the text snippets in a document are deemed delay-related. In these scenarios, if these delay-related snippets can be located, then attorneys could easily evaluate the model’s decision. The authors of this paper propose an approach for accurately identifying rationales and an approach for boosting document classification accuracy using delay-related snippets and their applications in construction delay disputes. The authors conducted experiments using data from a few real world delay dispute matters and the results from these experiments show that the proposed approaches have the potential to significantly advance the application of text classification in document review in construction delay dispute matters.

Predictive modeling - known in the legal industry as ‘predictive coding’ or ‘Technology Assisted Review (TAR)’ - is a popular tool used by legal professionals to augment a historically manual document review and classification process in responding to data requests for various legal proceedings. It has become more commonplace over the past decade due to its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings. There is significant research supporting the effectiveness of this technology that includes topics, such as identifying the most performant machine learning algorithms (e.g., logistic regression) or establishing the best methodologies for selecting representative training and testing data. Primarily, this research has been performed without a focus on multilanguage data sets because many legal proceedings typically involve one primary, dominant language. As acceptance of predictive modeling technology grows, legal practitioners have developed independent preferences for handling multiple languages in data sets. These preferences have been primarily based on anecdotal experience rather than empirical assessments and have resulted in two modeling approaches. The first approach uses a single model, which is less complex, and more cost effective than the next approach. The second develops multiple language-specific models, which can create workflow complexity that may impact the overall cost savings that predictive coding seeks to achieve. Proponents of the second approach believe that creating models per language results in better performance over a single model approach. In this study, we empirically explore the performance differences between the two approaches - single model and multiple language-specific models. We hypothesize that a single model approach performs similarly to a language-specific modeling approach when classifying documents for relevance. We evaluate both approaches by comparing their resulting precision-recall curves across real-world test data from legal document review projects. Excitingly, our results demonstrate that in most scenarios, a single model (trained with multiple languages) approach will perform as well, or sometimes better than the approach that uses a group of language-specific models. The results of our research provide a roadmap for legal practitioners and other parties to thoughtfully engage in dialogue around the most effective way to deploy predictive coding within a multilanguage data set. This research will change how predictive models are deployed for multilanguage data sets across the legal industry and enable significant cost savings for clients and legal practitioners by utilizing the most efficient modeling method.

Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communication or litigation strategy, is exempt from disclosure in litigation or regulatory events and is vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel must review vast amounts of documents to determine those that contain privileged material. This process is costly and time consuming. As data volumes increase, legal counsel employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is a popular method to target privileged information and reduce document review populations. Keyword terms are effective at casting a wide net but generally return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams have started using supervised learning techniques to more precisely target privileged information. However, reviewing and labeling training documents is costly and time intensive and may cause counsel to forego the use of supervised learning in certain scenarios. In addition, supervised learning techniques may not find all the privileged documents in a document review and require companies to use keyword terms to identify critical privileged information. In this paper, the authors propose a novel method to automatically identify privileged documents without the need to label new training documents. This method integrates rule-based reasoning with transfer learning. Experimental results show that the proposed integrated method performs better than rule-based reasoning and transfer learning individually and can effectively identify privileged documents.

US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs of legal matters, it also faces a perception challenge: amongst lawyers, this technology is sometimes looked upon as a "black box." Put simply, no extra information is provided for attorneys to understand why documents are classified as responsive. In recent years, explainable machine learning has emerged as an active research area. In an explainable machine learning system, predictions or decisions made by a machine learning model are human understandable. In legal ‘document review’ scenarios, a document is responsive, because one or more of its small text snippets are deemed responsive. In these scenarios, if these responsive snippets can be located, then attorneys could easily evaluate the model’s document classification decisions – this is especially important in the field of responsible AI. Our prior research identified that predictive models created using annotated training text snippets improved the precision of a model when compared to a model created using all of a set of documents’ text as training. While interesting, manually annotating training text snippets is not generally practical during a legal document review. However, small increases in precision can drastically decrease the cost of large document reviews. Automating the identification of training text snippets without human review could then make the application of training text snippet-based models a practical approach. This paper proposes two simple machine learning methods to locate responsive text snippets within responsive documents without using human annotated training text snippets. The two methods were evaluated and compared with a document classification method using three datasets from actual legal matters. The results show that the two proposed methods outperform the document-level training classification method in identifying responsive text snippets in responsive documents. Additionally, the results suggest that we can automate the successful identification of training text snippets to improve the precision of our predictive models in legal document review and thereby help reduce the overall cost of review.

Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.

Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Over the past two decades, attorneys have been using a variety of technologies to conduct this exercise, and most recently, parties on both sides of the `legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, text classification also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a black box Put simply, very little information is provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In legal `document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if text classification can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision. When deployed with defined and explainable results, text classification can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.

In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review - where they are typically assessed for relevancy or privilege - the process can require companies to dedicate an extraordinary level of resources, both with respect to human resources, but also with respect to the use of technology-based techniques to intelligently sift through data. Companies regularly spend millions of dollars producing `responsive' electronically-stored documents for these types of matters. For several years, attorneys have been using a variety of tools to conduct this exercise, and most recently, they are accepting the use of machine learning techniques like text classification (referred to as predictive coding in the legal industry) to efficiently cull massive volumes of data to identify responsive documents for use in these matters. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In typical legal `document review' scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision. When deployed with defined and explainable results, predictive coding can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. The authors of this paper propose the concept of explainable predictive coding and simple explainable predictive coding methods to locate responsive snippets within responsive documents. We also report our preliminary experimental results using the data from an actual legal matter that entailed this type of document review. The purpose of this paper is to demonstrate the feasibility of explainable predictive coding in the context of professional services in the legal space.

Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communications or litigation strategy. are vital to the legal process and are exempt from disclosure in litigations or regulatory events. To protect this information from being disclosed, companies and outside counsel must review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel employ methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but return over inclusive results - most of which do not contain privileged information - and without detailed knowledge of the data, keyword lists cannot be crafted to find all privilege material. Overly-inclusive keyword searching can also be problematic, because even while it drives up costs, it also can cast `too far of a net' and thus produce unreliable results.To overcome these weaknesses of keyword searching, legal teams are using a new method to target privileged information called predictive modeling. Predictive modeling can successfully identify privileged material but little research has been published to confirm its effectiveness when compared to keyword searching. This paper summarizes a study of the effectiveness of keyword searching and predictive modeling when applied to real- world data. With this study, this group of collaborators wanted to examine and understand the benefits and weaknesses of both approaches to legal teams with identifying privilege material in document populations.