Introducing Custom-Trained LLMs: AI that Speaks Your Legal Language

Its initial use is based on GPT-3.5 tech, but to tap into GPT-4, one needs a Plus package deal. You might find ChatGPT too generic and want to train it on your own data. For instance, in marketing, a custom LLM application can continuously optimize advertising campaigns, adjusting keywords, targeting, and content to accommodate growth while maintaining efficiency. For example, in retail, a custom LLM application can integrate with your CRM system to enhance customer profiles, allowing for more effective loyalty program management and personalized marketing campaigns.

The age of having human agents for tasks that are automated now has passed. AI and LLM solutions are increasingly making their way into the world. It is best for businesses to transform themselves to incorporate the changing technology into their models to stay at the top of the game with competitive edges.

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This means the model can learn more quickly and accurately from smaller, labeled datasets, reducing the need for large labeled datasets and extensive training for each new task. Transfer learning can significantly reduce the time and resources required to train a model for a new task, making it a highly efficient approach. Autoencoding models are commonly used for shorter text inputs, such as search queries or product descriptions. They can accurately generate vector representations of input text, allowing NLP models to better understand the context and meaning of the text. This is particularly useful for tasks that require an understanding of context, such as sentiment analysis, where the sentiment of a sentence can depend heavily on the surrounding words.

On the other hand, BERT has been trained on a large corpus of text and has achieved state-of-the-art results on benchmarks like question answering and named entity recognition. This comprehensive resource suite simplifies the process and makes it accessible to a wider audience, ushering in a new era of AI-driven natural language understanding and generation. Whether you’re a seasoned AI practitioner or just starting, these tools allow you to explore the fascinating world of language models and their applications. In recent years, large language models (LLMs) like OpenAI’s GPT series have revolutionized the field of natural language processing (NLP). These models are capable of generating human-like responses to a variety of prompts, making them a valuable asset for businesses.

Claude 2.1 vs ChatGPT: Which AI is better?

Smaller, more domain-specific models can be just as transformative, and there are several paths to success. OpenLLM’s support for a diverse range of open-source LLMs and LlamaIndex’s ability to seamlessly integrate custom data sources provide great customization for developers in both communities. This combination allows them to create AI solutions that are both highly intelligent and properly tailored to specific data contexts, which is very important for query-response systems. Deployment of functional data products to be utilized by the business is pivotal, as it enhances the value and impact of the LLM. Luckily, we can use no-code Dataiku Applications to package our project as a reusable application with predefined actions to build our Flow with new inputs and view the results. The application below shows how no-code users can simply ask or upload a batch of questions, run a scenario to submit these questions for inference, and view and/or download the generated answers from Dolly.

Testing large language models in production helps ensure their robustness, reliability, and efficiency in serving real-world use cases, contributing to trustworthy and high-quality AI systems. We also perform error analysis to understand the types of errors Custom Data, Your Needs the model makes and identify areas for improvement. For example, we may analyze the cases where the model generated incorrect code or failed to generate code altogether. We then use this feedback to retrain the model and improve its performance.

How to create your own Large Language Models (LLMs)!

GPT-J is a model from EleutherAI trained on six billion parameters, which is tiny compared to ChatGPT’s 175 billion. It mentions it wants to be the “best instruction-tuned assistant-style” language model. We followed the same in context learning approach when we crafted the prompt and added internal knowledge to ChatGPT in the prompt.py.

An ROI analysis must be done before developing and maintaining bespoke LLMs software. For now, creating and maintaining custom LLMs is expensive and in millions. Most effective AI LLM GPUs are made by Nvidia, each costing $30K or more. Once created, maintenance of LLMs requires monthly public cloud and generative AI software spending to handle user inquiries, which can be costly. I predict that the GPU price reduction and open-source software will lower LLMS creation costs in the near future, so get ready and start creating custom LLMs to gain a business edge. On-prem data centers, hyperscalers, and subscription models are 3 options to create Enterprise LLMs.

Why do you need private LLMs?

While there is room for improvement, Google’s MedPalm and its successor, MedPalm 2, denote the possibility of refining LLMs for specific tasks with creative and cost-efficient methods. General LLMs are heralded for their scalability and conversational behavior. Everyone can interact with a generic language model and receive a human-like response. Such advancement was unimaginable to the public several years ago but became a reality recently. With APIs, document embedding and vector indexing runs continuously behind the scenes on managed infrastructure.

If your task falls under text classification, question answering, or Entity Recognition, you can go with BERT. For my case of Question answering on Diabetes, I would be proceeding with the BERT model. It can be helpful to test each mode with questions specific to your knowledge base and use case, comparing the response generated by the model in each mode. Enhancing your LLM with custom data sources can feel overwhelming, especially when data is distributed across multiple (and siloed) applications, formats, and data stores.

On top of that I’ve got 300gb of text extracted from court documents and that’s just from California! I haven’t even downloaded the federal dump yet, let alone the other 49 states. Even if I limited myself to just the US Code and Federal Code of Regulations, that’s hundreds of millions of tokens of very dense text. Embedding based RAG has been pretty much useless in each of these cases but maybe I just suck at implementing Custom Data, Your Needs the retrieval part. We are working on real-time large-scale projects like teaching LLMs to understand the news as it breaks as part of louie.ai, and if folks have projects like that, happy to chat as we figure out our q1+q2 cohorts. It’s a fascinating time — we had to basically scrap our pre-2023 stack here because of the significant advances, and been amazing being able to tackle much harder problems.

The code is kind of a mess (most of the logic is in an ~8000 line python file) but it supports ingestion of everything from YouTube videos to docx, pdf, etc – either offline or from the web interface. It uses langchain and a ton of additional open source libraries under the hood. It can run directly on Linux, via docker, or with one-click installers for Mac and Windows. You could make a recursive also where you parse all the chunks, generate summaries of those and then pass them to the next chunk sequentially and so on. All of the models in one place (apart from the big dogs from OpenAI) and a common API across them. I used “” and + and – for terms to get what I want, and its search engine still gives you the sponsored results and an endless list of matches based on what you might buy instead of what you searched for.

Steps to build an LLM for your company’s data (DIY Model)

Base models are trained on publicly available internet data, not on a law firm’s private documents, a wealth manager’s research reports, or an accounting firm’s financial statements. This specific data and context is the key to helping a model go from generic responses to actionable insights for specific use cases. General-purpose large language models are jacks-of-all-trades, ready to tackle various domains with their versatile capabilities. Below diagram explains conceptually how these embeddings are used to retrieve information from your documents using LLM.

The more dimensions the embedding has, the more features it can learn. We can take advantage of the system messages’ ability to restrict the LLMs functionality by providing custom information in the system message. We can then have the LLM use only this information when responding to queries, avoiding the risk of the LLM hallucinating information. This is how RAG works to answer queries, as shown in the diagram below. Also maybe try to include tags or categories when you index and then you can filter on those when doing the vector search. For the second (RAG or similar), fire up a cloud VM with GPUs or use Ollama locally and read through the LlamaIndex docs on how to build a RAG pipeline.

For model performance, we monitor metrics like request latency and GPU utilization. For usage, we track the acceptance rate of code suggestions and break it out across multiple dimensions including programming language. This also allows us to A/B test different models, and get a quantitative measure for the comparison of one model to another. Details of the dataset construction are available in Kocetkov et al. (2022). Following de-duplication, version 1.2 of the dataset contains about 2.7 TB of permissively licensed source code written in over 350 programming languages. LangChain is a framework that provides a set of tools, components, and interfaces for developing LLM-powered applications.

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