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NextLaw.Pro Advantages: More Relevant Responses from LLM
NextLaw.pro use artificial intelligence (AI) to retrieve information and analyses from large language models (LLMs) such as Open AI’s Chat GPT.
Getting more relevant responses from ChatGPT or any other large language model (LLM), often requires a combination of prompt engineering, context provision, and understanding the model’s behavior.
What Artificial Intelligence Is
Artificial Intelligence (AI) refers to machines that are programmed to rationalize and take actions that have the best chance of achieving a specific goal.
AI is a broad field of study that includes many theories, methods, and technologies, as well as the following subfields:
- Machine Learning:
- Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Deep learning is a subset of machine learning characterized by layers of neural networks.
- Neural Networks:
- Inspired by the biological neural networks that constitute animal brains, these systems can learn to perform tasks by considering examples, generally without being programmed with task-specific rules.
- Natural Language Processing (NLP):
- This involves the ability of computers to analyze, understand, and generate human language, including speech. This is exhibited in your interaction with me right now.
What Artificial Intelligence Does
Artificial intelligence encompasses a multitude of functions and capabilities which can be quite diverse, based on the application and domain of use. Here’s a look at what some of the things that AI can do :
- Learning: AI systems, through machine learning, can digest large amounts of information and learn patterns and features from the data. It can involve such tasks as object recognition, speech recognition, translation, and even game playing.
- Reasoning: AI can be used to take new information and infer the logical consequences of that data. This involves applying rules to the data to reach approximate or definite conclusions. Expert systems, for instance, are an example of AI that can reason to make informed decisions.
- Problem-Solving: AI systems can be programmed to solve problems that usually require human intelligence. These can range from complex calculations to logical problem-solving, strategic planning, or pattern recognition in data.
What Large Language Models Are
Large language models are a type of artificial intelligence software designed to understand and generate human-like text by analyzing and predicting the probabilities of language sequences. These models are “large” both in terms of the architecture (the number of parameters they contain) and the amount of data they are trained on. They fall under the broader category of machine learning and, more specifically, natural language processing (NLP).
Here are some key components and characteristics that define large language models:
- Parameters: In the context of machine learning, a parameter is a configuration variable that is internal to the model and whose value can be estimated from data. The “largeness” of a language model generally refers to the number of parameters it has. For example, GPT (Generative Pre-trained Transformer) models, developed by OpenAI, have versions that range from millions to hundreds of billions of parameters. These parameters are weights in the neural network that are adjusted during training to help the model make accurate predictions.
- Deep Learning and Neural Networks: Large language models are typically built using deep learning, a subset of machine learning that involves neural networks with many layers (hence the term “deep”). These models use structures called transformers, which allow them to handle sequential data (like language) while taking into account the context and relationships between words or phrases.
- Training Data: The quality and breadth of a language model’s responses are heavily reliant on its training data. These models are trained on vast datasets taken from diverse sources, such as books, websites, and other texts, to learn the statistical properties and nuances of a language. The more extensive and diverse the training data, the more robust the model is in understanding and generating text that is coherent and contextually relevant.
- Pre-training and Fine-Tuning: Large language models often undergo a two-stage training process. The first stage, pre-training, involves training the model on a large text corpus to learn language structures and semantics. The second stage, fine-tuning, involves further training the model on a smaller, more specific dataset to specialize it for particular tasks, such as translation, question-answering, or text generation.
- Capabilities: These models are designed to perform a wide range of language tasks, such as translation, summarization, question answering, and text completion. They’re also capable of generating coherent, contextually relevant, and grammatically correct text passages. Their proficiency in understanding context and generating text makes them suitable for conversational AI applications, like chatbots or virtual assistants.
- Limitations and Challenges: Despite their capabilities, large language models have limitations. They can propagate bias present in their training data, and they often struggle with tasks requiring multi-step reasoning or deep understanding of the world. They are also data and resource-intensive, requiring significant computational power for training and use, which brings environmental and accessibility concerns.
- Ethical Considerations: The deployment of large language models raises ethical questions. Their ability to generate text makes it possible to produce misinformation, spam, or offensive content at scale. Moreover, concerns about privacy arise when models inadvertently memorize sensitive information from the training data.
Large language models represent a significant advancement in the ability for machines to process and generate human language, offering a wide range of practical applications as well as posing new technical and ethical challenges. They continue to be a rapidly evolving technology, pushing the boundaries of what’s possible with artificial intelligence.
What Large Language Models do
When large language models, like GPT-3 or GPT-4, respond to legal questions, they don’t “think” or “understand” content as humans do. Instead, they generate responses based on patterns in the data they were trained on. Here’s a more detailed view of what happens when you ask a legal question:
- Interpreting the Query: The model parses the question, identifying key terms, context, and the nature of the query. This process is based on the model’s training, which includes exposure to countless text samples that help it understand the structure of the language and common patterns within legal contexts.
- Generating a Response: Using the patterns it has learned from the training data, the model predicts the most likely sequence of words to follow the input query in a way that is grammatically coherent and contextually relevant to the provided prompt. The response is generated word by word, with each new word being selected based on its probability to follow the previous sequence of words, until the model determines that the response is complete.
- Reliance on Training Data: The quality of the response heavily depends on the breadth and quality of the training data. If the model has been trained on extensive legal texts, it’s more likely to produce a relevant and accurate answer. However, because the model generates responses based on the input and what it “thinks” is the most likely continuation, it can sometimes produce inaccurate or misleading information, especially for complex or niche legal topics.
- No True Understanding or Reasoning: It’s important to note that while the model can generate responses that sound logical and knowledgeable, it doesn’t truly “understand” the content or the implications of the advice it provides. It doesn’t reason like a human lawyer; it mimics patterns of reasoning it learned during training.
- Lack of Current and Specific Knowledge: Large language models can’t access or retrieve information beyond their training data. This means they can’t pull the latest legal statutes or case law, nor can they provide insights into ongoing court cases unless this information was included in their training data up to the last update.
- Ethical and Practical Considerations: There are significant ethical considerations when using AI for legal advice. Misinterpretations or inaccuracies in legal matters can have serious consequences. Therefore, it’s recommended that individuals seek advice from qualified legal professionals. Additionally, privacy is a concern, as sensitive information shared with the model could potentially be accessed by others, depending on the platform’s data security measures.
In summary, while large language models can generate responses to legal questions based on patterns they’ve learned during training, they don’t actually “know” the law or understand the nuances of legal practice. They should not be used as a substitute for professional legal advice. Their role might be best suited to providing general information on legal topics, assisting with legal research by pointing to potentially relevant statutes or case law, or helping draft routine documents under the supervision of legal professionals.
Some Techniques that Nextlaw.pro Uses to Better Retrieve Info from Large Language Models
Here are some of the techniques that NextLaw.pro uses:
- Provide Context: Since users are looking for legal information on a specific topic or within a particular framework, NextLaw.pro starts by providing a brief context to guide the LLM model to generate responses that align with your intent. Since legal issues are nuanced topics, NextLaw.pro provides relevant background or context to better guide the model.
- Use System Messages: NextLaw.pro uses system messages to set a context or guide the model’s behavior throughout the conversation.These system-level instructions set a context or behavior for the model. For example, a system message like “You are an expert in 19th-century literature” can guide the model’s responses in that direction.
- Prompt Engineering: NextLaw.pro helps craft prompts more carefully. Being clear, specific, and direct can help in getting the desired answer. The more specific and clear your question, the more accurate and relevant the response will likely be.
- Step-by-Step or Debate Pros and Cons: NextLaw.pro often instructs a model to reason step-by-step or list pros and cons before settling on a conclusion. This can provide a more comprehensive response.
- Limit Bias and Speculation: Nextlaw.pro often instruct the model to avoid biases or not to speculate. For instance, “Provide a fact-based overview of climate change without speculating.”
- Specify the Format: NextLaw.pro often instructs the model on a preference for the way that the information should be presented (e.g., a summary, a list, pros and cons).
- Iterative : In the forms based queries, NextLaw.pro breaks down the issue or question into smaller component parts and then queries more specific questions. This can help in gathering detailed and relevant information piece by piece.
- Experiment and Iterate: NextLaw.pro has tested various approaches. Although the models change, NextLaw.pro understands how ChatGPT responds to different prompts and questions. While the behavior of LLMs can sometimes be unpredictable, NextLaw.pro continues to experiment with different prompts, instructions, and techniques to see what works best for a specific use case.
- Understand Biases and Limitations: NextLaw.pro is aware of many of the model’s biases and limitations which can help interpret its responses and minimizes potential pitfalls.
- Regularly Update Interactions: Language models, like ChatGPT, evolve over time as they are fine-tuned with more data. NextLaw.pro endeavors to regularly update its interaction techniques to continue to get better responses from newer versions of the model.
- Stay Updated with Model Versions: Language models, including ChatGPT, are periodically updated by their developers. Newer versions might have improvements in accuracy, relevance, and reduced biases. NextLaw.pro helps you use the latest version or the one best suited to your needs.
- User Must Verify Information Retrieved with Known Reliable Source to Check for Accuracy and Truth. Remember, while ChatGPT and similar models are powerful, they’re not infallible. Nextlaw.pro reminds users that it is their responsibility to verify critical information from trusted sources and use the model as a tool rather than a definitive source of truth.
NextLaw.pro’s AI technology identifies patterns, connections, and nuances in vast amounts of data to give information and enable people to explore artificial intelligence and large language models.
This platform is intended for general informational purposes only and does not constitute legal advice or legal research. By using this service, you acknowledge and agree that NextLaw.pro is not engaged in the practice of law and does not provide legal advice.
NextLaw.pro does not provide legal research.
NextLaw.pro does not provide legal advice.
NextLaw.pro is not a substitute for attorneys. NextLaw.pro cannot act as an attorney. NextLaw.pro does not and cannot practice law.
Technical Specifications-Large Language Model
Currently, NextLaw.pro primarily retrieves information from the large language model Open AI’s ChatGPT 3.5 turbo via API. It is one of the industry standard LLM models.
To improve the privacy of the information, NextLaw.pro accesses ChatGPT via OpenAI’s application processing interface (API). OpenAI has indicated that it does not use the information received through the API to train its models. (Open AI apparently does train its models on the information entered in the chat directly on its web site.)
NextLaw.pro is agnostic about which LLM it may use. Different LLMs offer different advantages and disadvantages. One significant disadvantage of Open AI’s ChatGPT is that it is based on information before sometime in 2021. THerefore it lacks information generated in the last two years. This is likely to change with updated models.
We expect to use Open AI ChatGPT 4.0 fairly soon. Open AI has indicated that it will offer an enterprise and business version of ChatGPT soon.
NextLaw.pro may also use Anthropic’s Claude 2 (or later), Google’s Bard, Meta’s Llama 2 (or later)or other large language models.
Providing Context and Instructions
Providing context in a system message to a Large Language Model (LLM) means giving the model additional background or guiding information to help it generate more relevant, accurate, or specific responses. This context acts as a “primer” or “instruction” that sets the stage for the model’s understanding and subsequent generation.
In the case of models like OpenAI’s GPT series, the system message can be especially useful because these models don’t have a memory of past interactions. Each query is processed in isolation. By providing context, you can give the model a “temporary memory” or a frame of reference for the current interaction.
Key points about providing context to an LLM:
- Improves Relevance: By setting the stage with context, the model can generate responses that are more aligned with the user’s intent.
- Reduces Ambiguity: Context helps in narrowing down the possible interpretations of a user’s query, leading to more precise answers.
- Guides the Model: Especially in nuanced or specialized domains, context can guide the model to think in a specific direction or framework.
- Enhances User Experience: Users don’t have to repeatedly provide the same information, as the context can carry essential background information throughout the interaction.
- Dynamic Adaptation: System messages can be dynamically adjusted based on the flow of the conversation or the needs of the application, allowing for flexible interactions.
In essence, providing context helps the model understand the user’s perspective better and generate more appropriate responses.
NextLaw.pro engineers the prompts and provides context to the LLM queries.
Prompt engineering refers to the process of carefully crafting and refining input prompts to optimize the performance of machine learning models, especially language models like those based on the GPT (Generative Pre-trained Transformer) architecture. The goal is to elicit the most accurate, relevant, or desired response from the model.
In the context of language models, a prompt is the input text that you provide to the model, and the model’s response is based on this input. By tweaking the phrasing, structure, or content of the prompt, you can influence the model’s output.
Here are some key points about prompt engineering:
- Guiding the Model: A well-engineered prompt can guide the model towards generating a specific type of response or thinking in a particular context. For instance, asking a model “What is the capital of France?” versus “Name the city in Europe where the Eiffel Tower is located” might both yield the answer “Paris,” but the latter provides more context.
- Improving Accuracy: For tasks where precision is crucial, such as in medical or legal contexts, prompt engineering can help in obtaining more accurate or cautious answers from the model.
- Handling Ambiguity: A well-crafted prompt can help reduce ambiguity in the model’s response. For instance, instead of asking “Tell me about Apple,” which could lead to information about the fruit or the tech company, a more specific prompt like “Tell me about Apple Inc.’s latest products” would yield more targeted results.
- Iterative Process: Prompt engineering often involves an iterative process of testing and refining prompts to achieve the desired output. This can be especially important when deploying models in real-world applications where the quality of the output is crucial.
- Fine-Tuning vs. Prompt Engineering: While fine-tuning involves retraining a model on specific data to adapt it to particular tasks, prompt engineering doesn’t change the model itself but rather optimizes the input to get the best possible output.
- Cost-Effective: Prompt engineering can be a cost-effective way to improve model performance without the need for additional training or computational resources.
In summary, prompt engineering is a technique used to optimize the interaction with machine learning models, especially large language models, by carefully crafting the input prompts to achieve desired outputs. It’s an essential skill for developers and researchers working with these models to ensure they extract maximum value and accuracy from them.
Privacy and Storage of Information of Chat- No Chat Records Maintained
NextLaw.pro uses different systems and methods in its chat based information retrieval service versus its form based service.
Nextlaw.pro does not retain and does not store the information input into the chat functions.
NextLaw.pro does not retain and does not store any data generated by the chat functions.
While transient information is momentarily held and transferred during processing,Nextlaw.pro does not create records of the chat information (other than retaining records of the existence of the subscribers).
Privacy and Storage of Information in Form-Based Submissions
Because the form-based queries can involve more sophisticated prompt engineering, Nextlaw.pro does retain records of the information input into the forms and the information generated by the LLM.