Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots

The Substack focuses on large and small language models, natural language understanding, chatbots, and conversational user interfaces. It covers AI agent applications, methods for improving AI performance, and practical tools for developers. Themes include AI decision-making, fine-tuning, data design, and enhancing user-AI interaction.

Large Language Models Small Language Models Natural Language Understanding Chatbots Conversational User Interfaces AI Agents AI Fine-Tuning Data Design AI Interaction

The hottest Substack posts of Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots

And their main takeaways

LangGraph Cloud

19 implied HN points • 02 Jul 24
🕹 Technology AI Software Development Cloud Computing NLP
  1. LangGraph Cloud is a new service that helps developers easily deploy and manage their LangGraph applications online.
  2. Agent applications can handle complex tasks automatically and use large language models to work efficiently, but they face challenges like high costs and the need for better control.
  3. LangGraph Studio provides a visual way to see how code flows in applications, helping users understand and debug their work without changing any code.

Challenges In Adopting Retrieval-Augmented Generation Solutions

59 implied HN points • 01 Apr 24
🕹 Technology AI Development Language Models Data Management User Experience Privacy Concerns Software Engineering
  1. Retrieval-Augmented Generation (RAG) uses contextual learning to improve responses and reduce errors, making it useful for Generative AI.
  2. RAG systems are easier to maintain and less technical, which helps keep them updated with changing needs.
  3. However, RAG can have shortcomings like poor retrieval strategies and issues with data privacy, leading to incomplete or incorrect answers.

Proxy Fine-Tuning LLMs

79 implied HN points • 26 Feb 24
🕹 Technology AI Machine Learning Data science Software Development
  1. Proxy fine-tuning lets you improve a language model's performance without changing its internal settings. It only uses the model's output to make adjustments.
  2. Combining different approaches, like retrieval and fine-tuning, can lead to better results with language models. It's about using the best methods together instead of relying on just one.
  3. Using proxy fine-tuning can help organizations better understand and organize their data. It encourages them to explore their information needs more deeply.

A Short History Of Chatbots

39 implied HN points • 09 May 24
🕹 Technology AI Chatbots Natural Language Machine Learning Development
  1. Chatbots have changed a lot over time, starting as simple rule-based systems and moving to advanced AI models that can understand context and user intent.
  2. Early chatbots used basic pattern recognition to respond to user questions, but this method was limited and often resulted in repetitive and predictable answers.
  3. Now, modern chatbots utilize natural language understanding and machine learning to provide more dynamic and relevant responses, making them better at handling various conversations.

FlowMind Is An Automatic Workflow Generator

19 implied HN points • 25 Jun 24
🕹 Technology AI Automation Workflow Data science Programming
  1. FlowMind is a new tool that helps create automatic workflows using advanced AI. It takes user requests and generates code to complete tasks quickly.
  2. The system uses APIs to gather information and provides real-time feedback, allowing users to adjust the workflows as needed. This makes the process more interactive.
  3. FlowMind aims to improve the reliability of AI by reducing errors and making sure there is no direct connection to sensitive data. It focuses on keeping user data safe while handling requests.
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Can Conversation Designers Excel As Data Designers?

19 implied HN points • 24 Jun 24
🕹 Technology AI Natural Language Machine Learning User Experience
  1. Conversation designers can play a key role in creating and improving datasets for training language models. Their skills can help make data more relevant and useful.
  2. Techniques like Partial Answer Masking and Prompt Erasure help models learn to self-correct and think strategically. This makes them better at reasoning and understanding complex tasks.
  3. Chain-of-Thought methods help language models break down problems into smaller steps. This approach can lead to more accurate and reliable answers.

Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

59 implied HN points • 11 Mar 24
🕹 Technology AI Language Models Open Source Machine Learning Data science
  1. Small Language Models (SLMs) can effectively handle specific tasks without needing to be large. They are more focused on doing certain jobs well rather than trying to be everything at once.
  2. The Orca 2 model aims to enhance the reasoning abilities of smaller models, helping them outperform even bigger models when reasoning tasks are involved. This shows that size isn't everything.
  3. Training with tailored synthetic data helps smaller models learn better strategies for different tasks. This makes them more efficient and useful in various applications.

Large Impact: The Rise of Small Language Models

59 implied HN points • 07 Mar 24
🕹 Technology AI Machine Learning Open Source Data Privacy Generative AI Natural Language
  1. Small Language Models (SLMs) are becoming popular because they are easier to access and can run offline. This makes them appealing to more users and businesses.
  2. While Large Language Models (LLMs) are powerful, they can give wrong answers or lack up-to-date information. SLMs can solve many problems without these issues.
  3. Using Retrieval-Augmented Generation (RAG) with SLMs can help them answer questions better by providing the right context without needing extensive knowledge.

DR-RAG: Applying Dynamic Document Relevance To Question-Answering RAG

19 implied HN points • 14 Jun 24
🕹 Technology AI Machine Learning NLP Data science
  1. DR-RAG improves how we find information for question-answering by focusing on both highly relevant and less obvious documents. This helps to ensure we get accurate answers.
  2. The process uses a two-step method: first, it retrieves the most relevant documents, then it connects those with other documents that might not be directly related, but still helps in forming the answer.
  3. This method shows that we often need to look at many documents together to answer complex questions, instead of relying on just one document for all the needed information.

Creating A Benchmark Taxonomy For Prompt Engineering

19 implied HN points • 13 Jun 24
🕹 Technology AI NLP Machine Learning Taxonomy Benchmarking
  1. Creating a standard system for evaluating prompts is important because prompts can vary in how they're used and understood. This makes it hard to measure their effectiveness.
  2. The TELeR taxonomy helps to categorize prompts so that they can be better compared and understood. It focuses on aspects like clarity and the level of detail in prompts.
  3. Using clear goals, examples, and context in prompts can lead to better responses from language models. This helps the models to understand exactly what is being asked.

Tree Of Thoughts Prompting (ToT)

19 implied HN points • 11 Jun 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Programming
  1. Tree of Thoughts (ToT) is a new way to solve complex problems with language models by exploring multiple ideas instead of just one.
  2. It breaks down problems into smaller 'thoughts' and evaluates different paths, similar to how humans think through problems.
  3. ToT allows models to understand not just the solution but also the reasoning behind it, making decision-making more deliberate.

Using Fine-Tuning To Imbed Hidden Messages In Language Models

19 implied HN points • 10 Jun 24
🕹 Technology AI NLP Machine Learning Language Models Software Development
  1. You can hide secret messages in language models by fine-tuning them with specific trigger phrases. Only the right phrase will reveal the hidden message.
  2. This method can help identify which model is being used and ensure that developers follow licensing rules. It provides a way to track model authenticity.
  3. The unique triggers make it hard for others to guess them, keeping the hidden messages secure. This technique also protects against attacks that try to extract the hidden information.

Putting AI To Work

39 implied HN points • 11 Apr 24
🕹 Technology AI Automation Software Data Interfaces
  1. AI tools can help businesses automate tasks and improve efficiency without needing coding skills. This makes it easier for companies to integrate AI into their workflows.
  2. It's important to have a single platform that can manage different AI models together. This way, organizations can create more effective applications by combining the strengths of various models.
  3. Moving AI projects from ideas to reality requires careful planning and testing. Organizations need to ensure models are well-trained before using them in real-world applications.

Implementing Chain-of-Thought Principles in Fine-Tuning Data for RAG Systems

19 implied HN points • 07 Jun 24
🕹 Technology Artificial Intelligence Natural Language Machine Learning Data processing Knowledge Management
  1. Using Chain-of-Thought principles can help language models improve how they think and respond. This means they can become better at understanding complex questions.
  2. Fine-tuning training data is being done in a more detailed way to enhance performance. This makes the models more efficient and effective in answering specific tasks.
  3. The goal of these improvements is to reduce errors, or 'hallucinations,' in responses. This way, the model can provide more accurate answers based on the information it retrieves.

FIT-RAG: Are RAG Architectures Settling On A Standardised Approach?

39 implied HN points • 02 Apr 24
🕹 Technology AI Architecture Data science Language Models Machine Learning
  1. As RAG systems evolve, they are integrating more smart features to enhance their effectiveness. This means they are not just providing basic responses but are becoming more advanced and adaptable.
  2. The challenges with RAG include static rules for retrieving data and the problem of excessive tokens during processing. These issues can slow down performance and reduce efficiency.
  3. FIT-RAG is addressing these challenges with new tools, like a special document scorer and token reduction strategies, to improve how information is retrieved and used. This helps RAG systems provide better answers while using fewer resources.

Comparing Human, LLM & LLM-RAG Responses

59 implied HN points • 09 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Healthcare Technology Research Studies Data science
  1. The study compared answers from humans, a basic LLM, and an LLM that uses RAG to see which is most accurate in healthcare. The LLM with RAG performed the best.
  2. Using RAG, the model was much quicker than humans, taking only about 15-20 seconds. Humans took around 10 minutes to respond.
  3. GPT-4, especially with RAG, showed high accuracy and can support doctors by providing fast and reliable answers, but humans should still check the information.

A New Study Compares RAG & Fine-Tuning For Knowledge Base Use-Cases

39 implied HN points • 25 Mar 24
🕹 Technology AI Machine Learning Data science Software Development Tech Trends
  1. Choosing technology depends on what you need to achieve. Focus on the specific requirements of the problem to find the right solution.
  2. Retrieval-Augmented Generation (RAG) is often more effective than Fine-Tuning for knowledge base tasks. It allows for quick searches and better accuracy.
  3. RAG systems are easier to update with new information compared to Fine-Tuned models. You can simply add new data without complex adjustments.

An Introduction To DSPy

19 implied HN points • 28 May 24
🕹 Technology Programming Machine Learning Software Development Artificial Intelligence Prompt engineering
  1. DSPy is a programming tool that simplifies how we work with language models by separating the tasks from the prompts. This means you tell DSPy what to do, not how to do it.
  2. It uses something called 'signatures' to describe tasks in a simple way, which helps in generating and optimizing prompts automatically. This reduces the need for manual prompt crafting.
  3. DSPy offers an iterative workflow for optimizing language tasks, making it suitable for complex applications. It can improve performance with minimal effort by tweaking how it uses language models.

A Short History Of RAG

39 implied HN points • 22 Mar 24
🕹 Technology AI Machine Learning Language Models Software Development Data processing
  1. Retrieval Augmented Generation (RAG) helps improve how language models work by adding context to their responses. This means they can give more accurate answers based on the information provided.
  2. Language models can show surprising abilities, called emergent capabilities, but these usually depend on the context they receive. If they get the right context, they can solve problems and adapt better.
  3. To get the best results from language models, it's important to provide them with the right information at the right time. This makes their answers more relevant and helps them understand what’s being asked.

Chain-of-Instructions (CoI) Fine-Tuning & Going Beyond Instruction Tuning

39 implied HN points • 21 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Chain-of-Instructions (CoI) fine-tuning allows models to handle complex tasks by breaking them down into manageable steps. This means that a task can be solved one part at a time, making it easier to follow.
  2. This new approach improves the model's ability to understand and complete instructions it hasn't encountered before. It's like teaching a student to tackle complex problems by showing them how to approach each smaller task.
  3. Training with minimal human supervision leads to efficient dataset creation that can empower models to reason better. It's as if the model learns on its own, becoming smarter and more capable through well-designed training.

Controllable Agents For RAG With Human In The Loop Chat

19 implied HN points • 27 May 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Automation Human-computer interaction
  1. Controllable agents improve how we interact with complex questions. They help make sense of complicated tasks by allowing step-by-step execution.
  2. Human In The Loop (HITL) chat lets users guide the process and provides feedback after each step. This means users can refine their inquiries live without long waits.
  3. The new tools from LlamaIndex aim to make working with large datasets easier by offering more control. This helps users monitor and adjust the process as needed.

Please Stop Saying Long Context Windows Will Replace RAG

39 implied HN points • 18 Mar 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. Long context windows (LCWs) and retrieval-augmented generation (RAG) serve different purposes and won’t replace each other. LCWs work well when asking multiple questions at once, while RAG is better for separate inquiries.
  2. Using LCWs can get really expensive because they involve processing a lot of data at once. In contrast, RAG uses smaller, focused data chunks, which helps keep costs down.
  3. Research shows that LLMs perform better when important information is at the start or end of a long context. So, relying only on LCWs can lead to problems since crucial details may get overlooked.

Concise Chain-of-Thought (CCoT) Prompting

59 implied HN points • 24 Jan 24
🕹 Technology AI Machine Learning Prompt engineering Natural Language Data Analysis
  1. Concise Chain-of-Thought (CCoT) prompting helps make AI responses shorter and faster. This means you save on costs and get quicker answers.
  2. Using CCoT, the response length can be reduced by almost 50%, but it can lead to lower performance in math problems. So, it’s a trade-off between speed and accuracy.
  3. For cost-saving in AI, focusing on reducing the number of output tokens is key since they are generally more expensive. CCoT is one way to achieve this without sacrificing performance too much.

How Would The Architecture For An LLM Agent Platform Look?

19 implied HN points • 24 May 24
🕹 Technology AI NLP Software Architecture Systems
  1. The architecture for an LLM agent platform could develop in three stages, starting with a simple AI that recommends tools based on user needs.
  2. As the platform grows, it will enable interactions between multiple tools and the AI, allowing for dynamic exchanges of information.
  3. Future improvements will focus on enhancing the agent's capabilities through better tools and more collaboration among them.

Can Minor Document Typos Comprehensively Disrupt RAG Retriever & Reader Components?

19 implied HN points • 20 May 24
🕹 Technology AI NLP Data Algorithms Machine Learning
  1. RAG systems can struggle with small mistakes in documents, making them vulnerable to errors. Even tiny typos can disrupt how well these systems work.
  2. The study introduces a method called GARAG that uses a genetic algorithm to create tricky documents that can expose weaknesses in RAG systems. It's about testing how robust these systems really are.
  3. Experiments show that noisy documents in real-life databases can seriously hurt RAG performance. This highlights that even reliable retrievers can falter if the input data isn’t clean.

Enterprise Prompt Engineering Practices

19 implied HN points • 17 May 24
🕹 Technology AI NLP Research Engineering Data
  1. Users spend a good amount of time, around 43 minutes, editing prompts to get better results from language models. They often make small, careful changes instead of big rewrites.
  2. The main focus of edits is usually on the context of the prompts, such as improving examples and grounding information. This shows that context is crucial for getting good outputs.
  3. Many users try multiple changes at once and sometimes roll back their edits. This indicates that they might struggle to remember what worked well in the past or which changes had positive effects.

GALE Is A Next-Gen Generative AI Productivity Suite

19 implied HN points • 15 May 24
🕹 Technology Artificial Intelligence Software Development Automation Productivity Tools Enterprise Solutions
  1. GALE is a new AI tool that helps businesses automate tasks. This saves time and allows employees to focus on important work.
  2. It allows users to create temporary applications for short-term projects, which can be discarded afterward. This is great for quick tasks without long-term commitment.
  3. GALE can save companies money by reducing repetitive work and improving efficiency. This helps businesses grow and innovate.

The Conversational AI Technology Landscape: Version 5.0

19 implied HN points • 14 May 24
🕹 Technology AI Chatbots Voicebots Natural Language Processing Machine Learning
  1. Voicebots add more complexity to chatbots, requiring new technologies like ASR and TTS. They need to handle issues like latency and background noise to provide a smooth experience.
  2. Agent desktops must integrate well with chatbots to improve customer service. This helps agents access information quickly and provides suggestions to handle customer interactions better.
  3. Cognitive search tools can enhance chatbots by allowing them to access a wider range of information. This helps them answer more diverse questions from users effectively.

How To Create A LangChain Application That Runs Locally & Offline

39 implied HN points • 28 Feb 24
🕹 Technology AI Software Development Privacy Data
  1. Running language models locally gives you more control over data privacy and enhances security by keeping sensitive information off external servers.
  2. Using small language models can improve efficiency in tasks like conversation management and language understanding while also cutting down on costs associated with cloud services.
  3. Local deployment makes models available offline, ensuring you can use them anytime without needing an internet connection, which is useful for research and development.

Language Model Quantization Explained

39 implied HN points • 27 Feb 24
🕹 Technology AI Machine Learning Natural Language Data Privacy Software Development
  1. Small language models can be very good at tasks like understanding language and generating text. They sometimes work better than bigger models because they can learn in context.
  2. Running language models locally can help with privacy and slow response times. This means businesses can customize their models while keeping data safer.
  3. Quantization helps make models smaller and quicker by summarizing their complex information. It’s like having condensed books that still have the important ideas.

Data Design For Fine-Tuning LLM Long Context Windows

19 implied HN points • 03 May 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. Fine-tuning large language models (LLMs) can help them better understand and use long pieces of text. This means they can make sense of information not just at the start and end but also in the middle.
  2. The 'lost-in-the-middle' problem happens because LLMs often overlook important details in the middle of texts. Training them with more focused examples can help address this issue.
  3. The IN2 training approach emphasizes that crucial information can be found anywhere in long texts. It uses specially created question-answer pairs to teach models to pay attention to all parts of the context.

Run A Small Language Model (SLM) Local & Offline

39 implied HN points • 14 Feb 24
🕹 Technology AI Machine Learning Data Privacy Software Development Chatbots
  1. Small Language Models (SLMs) can be run locally, giving you more control over your data and privacy. This means you can use them even without an Internet connection.
  2. SLMs are great for specific tasks that don't need the power of larger models, such as simple text generation or sentiment analysis. They can do a lot with less resource demand.
  3. Using SLMs can help businesses reduce costs related to API limits and data privacy issues. They also address delays that come with using larger models.

The Case For Small Language Models

39 implied HN points • 13 Feb 24
🕹 Technology AI Models Conversational AI Natural Language Processing Machine Learning Software Development
  1. Small Language Models (SLMs) can do many tasks without the complexity of Large Language Models (LLMs). They are simpler to manage and can be a better fit for common uses like chatbots.
  2. SLMs like Microsoft's Phi-2 are cost-effective and can handle conversational tasks well, making them ideal for applications that don't need the full power of larger models.
  3. Running an SLM locally helps avoid challenges like slow response times, privacy issues, and high costs associated with using LLMs through APIs.

Three Considerations For Private Open-Source LLM Instances

19 implied HN points • 29 Apr 24
🕹 Technology AI Software Open Source Data Privacy Machine Learning
  1. Large Language Models (LLMs) can struggle with performance over time. This problem affects apps that depend on commercial LLM APIs, leading to inconsistencies in how these applications work.
  2. Catastrophic forgetting is a challenge where LLMs forget earlier learned information when they learn new data. This can cause issues when the model is asked to understand broad topics.
  3. Hosting your own open-source LLMs gives your organization more control. You can manage updates, training, and data privacy, making your applications more secure and tailored to your needs.

Intents Are Not Going Away…RoNID Is A New Intent Discovery Framework

19 implied HN points • 26 Apr 24
🕹 Technology AI NLP Chatbots Machine Learning Data science
  1. RoNID helps identify user intents more accurately, allowing chatbots to understand what users really want to talk about. This means better conversations and less frustration.
  2. The framework uses two main steps: generating reliable labels and organizing data into clear groups. This makes it easier to see which intents are similar and which are different.
  3. RoNID outperforms older methods, improving the chatbot’s understanding by creating clearer and more accurate intent classifications. This leads to a smoother user experience.

UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue

39 implied HN points • 30 Jan 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data science Human-computer interaction
  1. UniMS-RAG is a new system that helps improve conversations by breaking tasks into three parts: choosing the right information source, retrieving information, and generating a response.
  2. It uses a self-refinement method that makes responses better over time by checking if the answers match the information found.
  3. The system aims to make interactions feel more personalized and helpful, leading to smarter and more relevant conversations.

Rapid Development Of Intelligent Generative AI APIs

19 implied HN points • 19 Apr 24
🕹 Technology AI APIs Development Software Automation
  1. Intelligent APIs use AI to add advanced features, making it easier for developers to integrate smart tech without deep knowledge of AI. They can improve apps in many areas like e-commerce and healthcare.
  2. Sometimes, just connecting an API to a language model isn't enough. It often needs extra logic or intelligence to function better, enhancing the user experience.
  3. The GALE platform helps automate tasks using generative AI, allowing businesses to streamline processes. This lets teams focus on more important and creative work.

Fine-Tuning OpenAI GPT-4o mini

2 HN points • 21 Aug 24
🕹 Technology AI Models Natural Language Machine Learning Data science Software Development
  1. OpenAI's GPT-4o Mini allows for fine-tuning, which can help customize the model to better suit specific tasks or questions. Even with just 10 examples, users can see changes in the model's responses.
  2. Small Language Models (SLMs) are advantageous because they are cost-effective, can run locally for better privacy, and support a range of tasks like advanced reasoning and data processing. Open-sourced options provide users more control.
  3. GPT-4o Mini stands out because it supports multiple input types like text and images, has a large context window, and offers multilingual support. It's ideal for applications that need fast responses at a low cost.

Data Design For Fine-Tuning To Improve Small Language Model Behaviour

19 implied HN points • 17 Apr 24
🕹 Technology AI Development Machine Learning Data science Natural Language Processing Model Training
  1. Small Language Models can be improved by designing their training data to help them reason and self-correct. This means creating special ways to present information that guide the model in making better decisions.
  2. Two methods, Prompt Erasure and Partial Answer Masking (PAM), help models learn how to think critically and correct mistakes on their own. They get trained in a way that shows them how to approach problems without providing the exact questions.
  3. The focus is shifting from just updating a model's knowledge to enhancing its behavior and reasoning skills. This means training models not just to recall information, but to understand and apply it effectively.

No-Code Deployment & Orchestration Of Open-Sourced Foundation Models

19 implied HN points • 16 Apr 24
🕹 Technology AI Automation Software Development Data
  1. Open-sourced language models are easier for everyone to access and can be customized to fit specific needs. This means more people, like researchers or developers, can use them to create unique solutions.
  2. Choosing the right model for each task can improve performance, so it's important to understand what each model does best. Using multiple models together can lead to better results overall.
  3. No-code tools like GALE make it simple to deploy and manage these models without needing deep technical skills. This helps businesses and individuals quickly set up and adapt AI applications.