The hottest Machine Learning Substack posts right now

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The Anatomy Of Chain-Of-Thought Prompting (CoT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Nov 23
🕹 Technology Machine Learning
  1. Chain-of-Thought (CoT) prompting helps large language models solve problems by breaking them down into smaller steps, just like humans do.
  2. For CoT to work well, the reasoning steps need to be ordered correctly and must be relevant to the question being asked.
  3. Even with incorrect reasoning, CoT can still perform well, showing that the overall method is more important than every single detail being perfect.

OpenAI String Tokenisation Explained

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Nov 23
🕹 Technology Machine Learning
  1. Tokenisation is the process of breaking down text into smaller pieces called tokens, which can be converted back to the original text easily. This makes it useful for understanding and processing language.
  2. Different OpenAI models use different methods for tokenising text, meaning the same input can result in different token counts across models. It’s important to know which model you are using.
  3. Using tokenisation can shorten the text length in terms of bytes, making the input more efficient. On average, each token takes up about four bytes, which helps models learn better.

Contrastive Chain-Of-Thought Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Nov 23
🕹 Technology Machine Learning
  1. Contrastive Chain-of-Thought Prompting (CCoT) improves reasoning by using both correct and incorrect examples. This helps the model identify mistakes better.
  2. CCoT is part of a broader trend that emphasizes the importance of complex, contextual data in training models. The way data is found and formatted is crucial for success.
  3. Creating automated methods for generating examples in CCoT can enhance the learning process. By showing positive and negative instances, models can learn what to avoid.

Knowledge-Driven Chain-of-Thought (KD-CoT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Nov 23
🕹 Technology Machine Learning
  1. The Knowledge-Driven Chain-of-Thought (KD-CoT) helps improve how language models answer questions by using knowledge from outside sources. This means better answers for complex questions.
  2. In-Context Learning (ICL) is important for language models. It allows them to use examples and context to provide more accurate and contextually relevant responses.
  3. Researchers are focusing on making language models better by using a human-in-the-loop approach, which means humans help guide and improve the model's ability to access and use data effectively.

The Chain-Of-X Phenomenon In LLM Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Nov 23
🕹 Technology Machine Learning
  1. Chain-of-thought prompting helps large language models break down complex problems. This makes it easier for them to solve tasks step by step, just like humans do.
  2. Using chain-of-thought techniques improves the transparency of LLMs. It allows users to see how the model arrives at its answers, which can reduce mistakes.
  3. Different prompting methods, like least-to-most prompting, can be combined with chain-of-thought techniques. This flexibility can enhance the performance of models in various tasks.
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Chain-Of-Note (CoN) Retrieval For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Nov 23
🕹 Technology Machine Learning
  1. Chain-of-Note (CoN) helps improve how language models find and use information. It does this by sorting through different types of information to give better answers.
  2. CoN uses three types of reading notes to keep responses accurate. This means it can better handle situations where the data isn’t directly answering a question.
  3. Combining CoN with data discovery and design is important for getting reliable information. This makes sure that language models work well in different situations.

LLM Hallucination Index

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Nov 23
🕹 Technology Machine Learning
  1. The LLM Hallucination Index helps measure how often AI models generate incorrect information. This is important for improving how these models perform tasks.
  2. Retrieval-Augmented Generation (RAG) significantly boosts the accuracy of AI responses by combining information retrieval and generation. It ensures the AI has better context for questions.
  3. Different AI models perform better on various tasks. OpenAI's GPT models are strong for Q&A and long-form text, while some smaller models can match their performance at a lower cost.

Are Emergent Abilities In LLMs Inherent Or Merely In-Context Learning?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Nov 23
🕹 Technology Machine Learning
  1. Emergent abilities in language models (LLMs) allow them to perform well on tasks they weren't specifically trained for. This shows a level of flexibility in handling diverse challenges.
  2. These abilities might not be hidden skills but rather show how LLMs learn through in-context examples. This means that understanding context plays a big role in their performance.
  3. As LLMs get larger and better, we see improvements in their skills, often influenced by new ways of giving them instructions, indicating that these skills can expand with better training techniques.

OpenAI Seeding, Model Fingerprints & Log Probabilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Nov 23
🕹 Technology Machine Learning
  1. The seed parameter helps in reproducing responses from an AI by combining it with the user prompt. This means if you want the same answer again, you need to use the same seed with the same question.
  2. System fingerprints are used to track changes in the AI model or environment. If the fingerprint changes, the responses might also change, so it’s important to keep track of this along with the seed.
  3. Log probabilities will be introduced to help understand which responses the AI is likely to give. This feature can be useful for improving things like search functions and suggestions.

Knowledge Retrieval Via The OpenAI Playground

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Nov 23
🕹 Technology Machine Learning
  1. OpenAI has introduced a Retrieval Augmentation tool in its Playground. This means the assistant can now find and use information from uploaded documents to answer questions better.
  2. When users upload a file, the assistant automatically processes it. It retrieves relevant content based on what the user asks and the context needed to give an answer.
  3. This feature aims to improve the assistant's performance while offering insights for better management. More controls and flexibility will be important as users need to customize how documents are handled.

What Are LLMs Good At & When Can LLMs Fail?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Nov 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) are great at generating clear and accurate text. They can produce sentences that make sense and are easy to read.
  2. LLMs are good at understanding language for tasks like sentiment analysis and answering questions. They can process and categorize text effectively.
  3. However, LLMs struggle with understanding complex ideas and real-world events. They can sometimes give incorrect or made-up information.

LLM Alignment, Hallucination & Misinformation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology Machine Learning
  1. It's important to have good data design and human supervision for large language models. This helps improve accuracy and creates better conversations.
  2. Large language models can produce different answers to the same question at different times. This means they are not always consistent.
  3. Misinformation and hallucinations can happen with these models, but we can reduce these issues by using better training and feedback methods.

Self-Refine Is An Iterative Refinement Loop For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology Machine Learning
  1. Self-Refine improves LLM output without needing extra training data. It does this by refining the output through feedback in a loop.
  2. The approach mimics how humans recheck their work to find better ways to express ideas, like improving an email draft or optimizing code.
  3. Quality of results gets better with more iterations, but it's important to balance this with potential delays and costs. Stronger models produce better refinements.

A New Prompt Technique From DeepMind Called Optimisation by PROmpting (OPRO)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology Machine Learning
  1. A new technique called Optimisation by PROmpting (OPRO) helps improve the performance of language models by using specific prompts. This method aims to make prompts more effective without changing the underlying models.
  2. OPRO can generate multiple prompt options at once, allowing the system to find the best one more efficiently. This strategy is helpful for solving tasks and provides better stability in results.
  3. The prompts created with OPRO can perform 8% to 50% better than those designed by humans, showing it can be more efficient in certain tasks. It's a new way to help machines understand and respond more accurately.

Large Language Models & The Problem Of Abundance

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 31 Oct 23
🕹 Technology Machine Learning
  1. Chatbot development has limited tools, making it hard to create flexible and intelligent systems. Developers often start from scratch, which can slow down progress.
  2. Large Language Models (LLMs) bring many features together, but the challenge is managing their overwhelming capabilities. Instead of building from nothing, developers must learn to control and direct LLMs effectively.
  3. There is a shift towards more general LLMs that can handle various tasks, making it easier to develop comprehensive applications. New techniques are also being created to better guide LLM responses.

Prompt Editing Based On User Feedback

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology Machine Learning
  1. Understanding user intent is crucial for Large Language Models (LLMs) to provide better responses. It helps in knowing what users really want.
  2. Using feedback from users can help improve the performance of LLMs in real-time. This means users can guide the model to understand their needs better.
  3. Adding context and clarity to prompts can significantly enhance how LLMs respond. By helping the model understand the situation better, we get more accurate answers.

Context-Aware Meta-Learning For Foundation Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology Machine Learning
  1. Large Language Models can learn quickly from little information during use, without needing extra training. This makes them very flexible in understanding and generating text.
  2. Currently, images don't learn as easily as text when it comes to recognizing new things on the spot. Improving this could allow visual models to learn like language models do.
  3. The new method called Context-Aware Meta-Learning helps visual models learn new concepts right away without extra setup. This can lead to exciting new applications that connect text and images better.

Data Delivery To Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Oct 23
🕹 Technology Machine Learning
  1. Data delivery is key to making large language models (LLMs) work well. It involves giving the model the right data at the right time to get accurate answers.
  2. There are two main stages for data delivery: during training and during inference. Training helps the model learn, while inference is when the model uses what it learned to respond to questions.
  3. A balanced approach is needed for data delivery in LLMs. Using different methods together will lead to better results than sticking to one single method.

The LangChain Implementation Of DeepMind’s Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Oct 23
🕹 Technology Machine Learning
  1. LangChain now has a way to use DeepMind's Step-Back Prompting, which helps improve how AI answers questions. It allows the AI to first rephrase a question into a simpler one before answering.
  2. This process involves creating examples to guide the AI on how to respond. The AI uses these examples to learn how to generate better questions and answers.
  3. You need some specific installations and an OpenAI API Key to try this out in a coding environment. Once set up, you can easily run the Step-Back Prompting in your projects.

A New Prompting Approach From DeepMind Called Analogical Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology Machine Learning
  1. DeepMind's Analogical Prompting helps language models recall similar past problems to solve new ones. This way, models can learn from existing knowledge without needing specific examples every time.
  2. This approach allows models to create their own relevant examples, reducing the need for human labeling and making the problem-solving process more efficient.
  3. By generating tailored examples, DeepMind's method improves the accuracy of solutions while also simplifying the training process for the models.

LLMs & Contextual Demonstration

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) learn from examples in a method called few-shot learning. This means they can understand and perform tasks based on just a few demonstrations.
  2. The effectiveness of LLMs in learning depends on how the input is organized, the types of labels used, and the format in which information is presented. These factors really matter for good performance.
  3. Using good prompts can dramatically improve how well smaller models work, even if they initially seem weak. Proper prompt engineering helps in making these models more effective for various tasks.

Large Language Model (LLM) Stack — Version 5

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Oct 23
🕹 Technology Machine Learning
  1. More open-source LLM models are available, letting people experiment and innovate. This is creating new opportunities for developers to explore different applications.
  2. No-code fine-tuning dashboards are making it easier for users to customize LLMs without technical skills. This expands the functionality of LLMs in various fields.
  3. Basic LLMs are replacing older products, and some advanced models are more at risk in this competitive landscape. This shift highlights the need for improved chat interfaces and prompt engineering techniques.

Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Oct 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) are evolving and diversifying, leading to the rise of Foundation Models that can handle various types of data like text and images. This means they can do more complex tasks now.
  2. There's a shift in how LLMs are used, with a focus on improving their functions like text analysis, speech recognition, and dialog generation. New techniques help these models perform better in their designated tasks.
  3. The market is seeing exciting new opportunities, especially in tools that help businesses use LLMs effectively, like data discovery and user-friendly interfaces. These tools can help companies tap into the potential of LLMs better.

A New Prompt Engineering Technique Has Been Introduced Called Step-Back Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Oct 23
🕹 Technology Machine Learning
  1. Step-Back Prompting helps Large Language Models find better answers by simplifying complex questions. It turns a detailed question into a more generic one that's easier to tackle.
  2. This technique can be combined with other methods to improve accuracy and effectiveness. It shows promise in fixing errors from traditional approaches.
  3. Using Step-Back Prompting requires careful thought and might work best with autonomous systems. It's a more advanced method compared to static prompting.

Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Oct 23
🕹 Technology Machine Learning
  1. Using Retrieval Augmented Generation (RAG) helps improve how language models work by allowing them to learn from additional, relevant data.
  2. RA-DIT is a new method that combines fine-tuning of the language model with updates to the retriever, making both more aligned and effective.
  3. A human approach to training the retriever with curated data ensures ongoing improvement and better responses in real conversations.

Can LLMs Outperform Humans At Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Oct 23
🕹 Technology Machine Learning
  1. Recent studies suggest that LLMs (large language models) may be better at creating prompts than humans. This means they can potentially get better results from the same tasks.
  2. The process called Automatic Prompt Engineering (APE) uses input and output examples to generate effective prompts without much human effort. It could change how we interact with LLMs in the future.
  3. Humans might not need to test many prompts anymore since LLMs can create tailored ones. This could make using AI easier and more efficient for everyone.

LLM Drift

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Sep 23
🕹 Technology Machine Learning
  1. LLM Drift refers to big changes in how language models respond over a short time. This means their answers can differ quite a bit unexpectedly.
  2. Studies show that the accuracy of models like GPT-3.5 and GPT-4 can go up and down significantly in just a few months. Sometimes they get worse at certain tasks.
  3. It's important to keep checking how these models behave over time because their performance can shift for many reasons, not just from minor tweaks.

RAG & Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology Machine Learning
  1. RAG, or Retrieval Augmented Generation, helps improve responses by adding relevant information to AI prompts. This makes the AI's answers more accurate and contextually appropriate.
  2. Fine-tuning adjusts the AI's behavior based on specific data, which can enhance its performance in certain fields like medicine or law. However, it may not always adapt well to unique user inputs.
  3. Using RAG alongside fine-tuning is the best approach. RAG is easier to implement and helps keep the AI's responses up-to-date while fine-tuning improves overall quality.

Emerging Large Language Model (LLM) Application Architecture

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Sep 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) work with unstructured data like human conversations. They generate natural language, but can sometimes give incorrect answers, known as 'hallucination.'
  2. Fine-tuning LLMs isn't popular anymore due to high costs and the need for constant updates. Instead, focusing on relevant prompts helps get better, accurate responses.
  3. Using multiple LLMs for different prompts makes sense. New tools are emerging to test how well different models work with specific prompts.

Agents, LLMs & Multihop Question Answering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Apr 23
🕹 Technology Machine Learning
  1. Agents can use different tools based on user requests. This gives them the flexibility to respond to questions that don't fit a typical sequence.
  2. Prompt chaining involves linking prompts together to create a more complex response. However, it can struggle with unexpected user queries.
  3. For better responses, it's important for an Agent to have clear instructions on which tool to use. Fine-tuning these instructions can improve how well the Agent answers questions.

Chain-Of-Thought Prompting In LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Apr 23
🕹 Technology Machine Learning
  1. Chain-of-thought prompting helps large language models break down complex tasks into smaller, manageable steps. This makes it easier for them to solve problems.
  2. Using chain-of-thought reasoning in prompts can improve how well language models perform on tasks by allowing them to show their reasoning process.
  3. This method is especially useful for tasks that require common sense or math, making it similar to how humans approach problem-solving.

Generative AI Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Apr 23
🕹 Technology Machine Learning
  1. Prompt pipelines make it easier to provide answers by using templates and adding specific context from a knowledge source. This helps to create better responses based on user requests.
  2. When a user asks something, the system finds the right template, fills in the necessary information, and sends it off to get a clear answer quickly.
  3. Using these pipelines helps to avoid mistakes by ensuring the information used is updated and accurate, rather than relying on potentially outdated data.

5 Things NLU Engines Are Really Good At

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Apr 23
🕹 Technology Machine Learning
  1. NLU engines make data entry super easy with no coding needed. You can just click and put in your data without worrying about complicated setups.
  2. Intents, or the goals of what users want, are flexible and can adapt to different classes or categories. This helps in understanding user requests better.
  3. Entities, which represent specific items or information, have improved a lot. Better detection of these lets chatbots gather information without having to ask the user again.

What Constitutes A Large Language Model Application?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Mar 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) are advanced AI tools that can understand and create human language. They help with tasks like writing, summarizing, and recognizing different pieces of information.
  2. There are different parts to building applications with LLMs. This includes using models, tools for development, and creating apps that end users can interact with.
  3. Prompt engineering is important for getting the best results from LLMs. It involves creating and managing prompts to guide the AI in generating useful responses.

Multi-Label Text Classification With Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Mar 23
🕹 Technology Machine Learning
  1. Google Cloud Vertex AI allows for multi-label text classification, which means multiple tags can be assigned to a document. This helps in better organizing and processing text data.
  2. Training a model on Vertex AI can take a long time, especially with large datasets. For example, using nearly 12,000 training items can take over four hours to complete.
  3. The system's interface for managing training data and labels can be complex and a bit confusing. This makes it harder to easily update and manage the training data.

Training & Testing Text Classification Models with Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Mar 23
🕹 Technology Machine Learning
  1. Google's AutoML makes it easy to build classification models without needing much technical know-how. It simplifies the process, allowing more people to create models.
  2. Vertex AI can classify text into single or multiple categories, but it doesn't support complex class structures. So, simple classifications work best.
  3. While AutoML speeds up model creation, training times can be long. It's important to plan your data splits and annotation sets for better model performance.

Creating Training Data For Text Classification In Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Mar 23
🕹 Technology Machine Learning
  1. Creating training data for AI is a crucial first step in making it work well. It involves careful organization and structuring of data to help the AI learn effectively.
  2. A data-centric approach requires ongoing exploration and refinement of the training data. This means continuously checking the data for patterns and making adjustments as needed.
  3. Using human labelers to categorize data can be costly and complex. It's often easier to automate this process with human oversight rather than sending data out for labeling.

Large Language Models, Generative AI & Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Mar 23
🕹 Technology Machine Learning
  1. Large Language Models (LLMs) have two sides: Generative and Predictive. Generative AI is popular for its ease of use, while Predictive AI requires specific training data and high accuracy.
  2. Google Cloud has focused on predictive AI before delving into generative AI. They offer tools for developers to create AI applications quickly, like chatbots and digital assistants.
  3. Classification is a key part of Predictive AI. It involves sorting input into predefined classes, which helps the model understand and respond accurately to user input.

A First Look At OpenAI GPT-4

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Mar 23
🕹 Technology Machine Learning
  1. GPT-4 is a step up from GPT-3.5, but the difference is mostly noticeable with complex tasks. For simple chat, you might not see much change.
  2. Currently, GPT-4 can't process images, but there's hope for that feature in the future. It'll be announced if it becomes available.
  3. One cool feature of GPT-4 is its ability to handle longer texts, over 25,000 words. This is great for detailed conversations or long content creation.

OpenAI Has Three New Use Modes, Each With Mode Specific Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Mar 23
🕹 Technology Machine Learning
  1. OpenAI has introduced three new modes for its language models. Each mode is designed for specific tasks like chat, insertion, and editing.
  2. These modes help users get better results by matching their tasks with the right model. Using the correct mode makes the AI work more effectively.
  3. Prompt engineering is now tailored to each mode. This means users will need to adjust their input templates to fit the specific needs of each mode.