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[co-author: Geoffrey Vance, Perkins Coie LLP]

[Foreword by Geoffrey Vance: Although this article is technically co-authored by Jan and me, the vast majority of the technical discussion is Jans work. And thats the point. Lawyers arent supposed to be the Captains of the application of generative AI in the legal industry. A lawyers role is more like that of a First Officer, whose responsibility is to assist the primary pilot in getting to the ultimate destination. This article and all the exciting parts within it demonstrate how important data scientists and advanced technology are to the legal profession. The law firms and lawyers who dont reach that understanding fast will be left behind. Those who do will lead the future of the legal industry.]

Contents

Why is Human Language so Hard to Understand for Computer Programs?What are Large Language Models?The Zoo of Transformer Models: BERT and GPTChatGPTs LimitationsHow to Improve Large Language ModelsIntegrating LLM with existing Legal TechnologyUnderstand the Decisions; XAI

Human language is difficult for computer programs to understand because it is inherently complex, ambiguous, and context-dependent. Unlike computer languages, which are based on strict rules and syntax, human language is nuanced and can vary greatly based on the speaker, the situation, and the cultural context. As a result, building computer programs that can accurately understand and interpret human language is exceptionally complex and has been an ongoing challenge for artificial intelligence researchers since AI was first introduced. This is exactly the reason why it took so long for humans (in many of our lifetimes) to create reliable computer programs to deal with human language.

In addition, for many different reasons, early language models took shortcuts and none of them addressed all linguistic challenges. It was not until Google introduced the Transformer model in 2017 in the ground-breaking paper Attention is all you need that a full encoder-decoder model, using multiple layers of self-attention, resulted in a model capable of understanding almost all of the linguistic challenges. The model soon outperformed all other models on various linguistic tasks such as translation, Q&A, classification, text-analytics.

Before we dive into the specifics of large language models, lets first look at the basic definition. Large Language Models are artificial intelligence models that can generate human-like language based on a large amount of data they have been trained on. They use deep learning algorithms to analyze vast amounts of text, learning patterns and relationships between words, phrases, and concepts.

Some of the most well-known LLMs are the GPT series of models developed by OpenAI, BERT developed by Google, and T5 developed by Google Brain.

As encoder-decoder models such as the T5 model are very large and hard to train due to a lack of aligned training data, a variety of cut-down models (also called a zoo of transformer models) have been created. The two best known models are: BERT and GPT.

ChatGPT is an extension of GPT. It is based on the latest version of GPT (3.5) and has been fine-tuned for human-computer dialog using reinforcement learning. In addition, it is capable of sticking to human ethical values by using several additional mechanisms. These two capabilities are major achievements!

The core reason ChatGPT is so good is because transformers are the first computational models that take almost all linguistic phenomena seriously. Based on Googles transformers, OpenAI (with the help of Microsoft) has shaken up the world by introducing a model that can generate language that can no longer be distinguished from human language.

Much to our chagrin, ChatGPT is not the all-knowing General Artificial Intelligence most would like it to be. This is mainly due to the decoder-only architecture. ChatGPT is great for chatting, but one cannot control the factuality. This is due to the lack of an encoder mechanism. The longer the chats, the higher the odds that ChatGPT will get off-track or start hallucinating. Being a statistical process, this is a logical consequence: longer sequences are harder to control or predict than shorter ones.

Using ChatGPT on its own for anything else than just casual chit-chatting, is not wise. Using it for legal or medical advice without human validation of the factuality of such advice is just dangerous.

The AI research is aware of this, and there are a number of on-going approaches to improve todays models:

Currently, the Artificial Intelligence industry is working on all of the above improvements. In addition, one can also expect integrations with other forms of human perception: vision and speech. As you may not know, OpenAI is also the creator of Whisper, the state of the art Speech recognition for 100s of languages and DALL-E2, the well-known image generator, so adding speech to the mix is only a matter of time.

If you made it this far, you should by now understand that ChatGPT is not by itself a search engine, nor an eDiscovery data reviewer, a translator, knowledge base, or tool for legal analytics. But it can contribute to these functionalities.

Full-text search is one of the most important tools for legal professionals. It is an integral part of every piece of legal software, assisting lawyers in case law search, legal fact finding, document template search, among other tasks.

Todays typical workflow involved formulating a (Boolean) query, ranking results on some form or relevancy (jurisdiction, date, relevance, source, etc.), reviewing the results, and selecting the ones that matter. As the average query length on Google is only 1.2 words, we expect our search engine to find the most relevant hits with very little information. Defining the query can be hard and will always include human bias (the results one gets depends on the keywords used). What is more, reviewing the results of the search query can be time consuming, and one never knows what one misses. This is where Chatbots can help: by changing the search process into an AI-driven dialogue, we can change the whole search experience.

This is exactly what Microsoft does with the BING ChatGPT integration, but with a few risks in the current implementation:

As explained earlier, more focus on explaining where the results come from, the ability to eliminate information and a better understanding of the meaning of the text used to drive the dialogue is probably needed to get better results. Especially when we plan to use this for legal search, we need more transparency and understanding where the results come from.

Contract drafting is likely one of the most promising applications of textual generative artificial intelligence (AI) because contracts are typically highly structured documents that contain specific legal language, terms, and conditions. These documents are often lengthy, complex, and require a high degree of precision, making them time-consuming and expensive to produce.

Textual generative AI models can assist in the drafting of contracts by generating language that conforms to legal standards and meets specific requirements. By analyzing vast amounts of legal data and identifying patterns in legal language, these models can produce contract clauses and provisions that are consistent with legal norms and best practices.

Furthermore, AI-generated contract language can help ensure consistency and accuracy across multiple documents, reduce the risk of errors and omissions, and streamline the contract drafting process. This can save time and money for lawyers and businesses alike, while also reducing the potential for disputes and litigation.

But, here too, we need to do more vertical training, and probably more controlled text generation by understanding and incorporating the structure of legal documents in the text-generation process.

In all cases, it is important to note that AI-generated contract language should be reviewed by a qualified lawyer to ensure that it complies with applicable laws and regulations, and accurately reflects the parties intentions. While AI can assist in the drafting process, it cannot replace the expertise and judgment of a human lawyer.

We have serious doubts if generative Artificial Intelligence can be used as it is and provide help in providing meaningful legal advice. AI models lack the ability to provide personalized advice based on a clients specific circumstances, or to consider the ethical and moral dimensions of a legal issue. Legal advice requires a deep understanding of the law and the ability to apply legal principles to a particular situation. Text generation models do not have this knowledge. So, without additional frameworks capable of storing and understanding such knowledge, using models such as ChatGPT is a random walk in the court.

E-discovery is a process that involves the identification, collection, preservation, review, and production of electronically stored information (ESI) in the context of legal proceedings. While e-discovery often involves searching for specific information or documents, it is more accurately described as a sorting and classification process, rather than a search process.

The reason for this is that e-discovery involves the review and analysis of large volumes of data, often from a variety of sources and in different formats. ChatGPT is unable to handle the native formats this data is in.

The sorting and classification process in e-discovery is critical because it allows legal teams to identify and review relevant documents efficiently and accurately, while also complying with legal requirements for the preservation and production of ESI. Without this process, legal teams would be forced to manually review large volumes of data, which would be time-consuming, costly, and prone to error.

In summary, e-discovery is a sorting and classification process because it involves the review and analysis of large volumes of data, and the classification and organization of that data in a way that is relevant to the legal matter at hand. While searching for specific information is a part of eDiscovery, it is only one aspect of a larger process.

ChatGPT is neither a sorting, nor a text analytical or search tool. Models such as BERT or text-classification models based on word-embeddings or TF-IDF in combination with Support Vector Machines are better, faster, and better understood for Assisted Review and Active Learning.

Where Generative AI can help, is in the expansion of search queries. As we all know, humans are always biased. When humans define (Boolean) search queries, the search keywords chosen by human operators are subject to this bias. Generative AI can be very beneficial assisting users defining a search query and come up with keywords an end-user would not have thought of. This increases recall and limits human bias.

Legal documents can be lengthy and often contain boiler plate text. Summarization can provide a quick overview of the most important aspects of such a document. GPT is very good at summarization tasks. This can assist reviewers or project managers to get faster understanding of documents in eDiscovery.

As an AI language model, ChatGPT could be used to draft written responses to eDiscovery requests or provide suggested language for meet and confer sessions. However, it cannot provide personalized legal advice or make strategic decisions based on the specific circumstances of a case.

eDiscovery platforms enrich, filter, order and sort ESI into understandable structures. Such structures are used to generate reports. Reports can be in either structured formats (tables and graphs), or in the form of description in natural language. The latter can easily be generated from the ESI database by using generative AI to create a more human form of communication.

Here too, we can state that ChatGPT is not a text analytical or search tool. Straight forward search engines (using keyword, fuzzy and regular expression search), or advanced text-classification models such as BERT are better, faster and better understood for compliance monitoring and information governance purposes.

Nobody is more interested in explainable Artificial Intelligence (XAI) than DARPA, the Defense Advanced Research Projects Agency. Already in 2016, DARPA started an XAI program.

Ever since, DARPA has sponsored various research projects related to XAI, including the development of algorithms and models that can generate explanations for their decisions, the creation of benchmark datasets for testing XAI systems, and the exploration of new methods for evaluating the explainability and transparency of AI systems.

XAI is one of the hottest areas of research in the AI community. Without XAI, the application of artificial intelligence is unthinkable in areas such as finance, legal, medical or military.

XAI, refers to the development of AI systems that can provide clear and transparent explanations for their decision-making processes. Unlike traditional black-box AI systems, which are difficult or impossible to interpret, XAI systems aim to provide human-understandable explanations for their behavior.

XAI is not a single technology or approach, but rather a broad research area that includes various techniques and methods for achieving explainability in AI systems. Some approaches to XAI include rule-based systems, which use explicit rules to generate decisions that can be easily understood by humans; model-based systems, which use machine learning models that are designed to be interpretable and explainable; and hybrid systems, which combine multiple techniques to achieve a balance between accuracy and explainability.

The development of XAI is an active area of research, with many academic and industry researchers working to develop new techniques and tools for achieving transparency and explainability in AI systems. Ultimately, the goal of XAI is to promote the development of AI systems that are not only accurate and efficient, but also transparent and trustworthy, allowing humans to understand and control the decision-making processes of the AI system.

For legal applications, a full XAI framework is essential. Without XAI, there can also not be legal defensibility or trust.

Vaswani, Ashish, et al. Attention is all you need. Advances in neural information processing systems 30 (2017).

Devlin, Jacob, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

Tenney, Ian, Dipanjan Das, and Ellie Pavlick. BERT rediscovers the classical NLP pipeline. arXiv preprint arXiv:1905.05950 (2019).

Radford, Alec, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language Models are Unsupervised Multitask Learners. (2019). GPT-2.

Language Models are Few-Shot Learners, Tom B. Brown et al., arXiv:2005.14165, July 2020. GPT-3.

Ouyang, Long, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155 (2022).

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Russell, Stuart (2017-08-31). Artificial intelligence: The future is superintelligent. Nature. 548 (7669): 520521. Bibcode:2017Natur.548..520R. doi:10.1038/548520a. ISSN 0028-0836.

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[1] Textual adversarial attacks are a type of cyber-attack that involves modifying or manipulating textual data in order to deceive or mislead machine learning models.

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