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

Retrieval-Augmented Generation (RAG) vs LLM Fine-Tuning

39 implied HN points • 19 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. Retrieval-Augmented Generation (RAG) is great for adding specific context and making models easier to use. It's a good first step if you're starting with language models.
  2. Fine-tuning a model provides more accurate and concise answers, but it requires more upfront work and data preparation. It can handle large datasets efficiently once set up.
  3. Using RAG and fine-tuning together can boost accuracy even more. You can gather information with RAG and then fine-tune the models for better performance.

The Case For An AI Productivity Suite

19 implied HN points • 12 Apr 24
🕹 Technology AI Tools Automation Productivity Data Management Business Processes
  1. An AI productivity suite helps people and businesses work more efficiently by combining tools for tasks like data analysis and automation.
  2. It allows users to automate regular tasks, freeing them to focus on more important work, and offers easy customization through no-code options.
  3. These suites also promote teamwork by improving communication and sharing among team members, leading to better project outcomes.

Step-Wise Controllable Agents From LlamaIndex

19 implied HN points • 10 Apr 24
🕹 Technology AI Chatbots NLP Autonomous Agents Software Development
  1. LlamaIndex has introduced a new agent API that allows for more detailed control over agent tasks. This means users can see each step the agent takes and decide when to execute tasks.
  2. The new system separates task creation from execution, making it easier to manage tasks. Users can create a task ahead of time and run it later while monitoring each stage of execution.
  3. This step-wise approach improves how agents are inspected and controlled, giving users a clearer understanding of what the agents are doing and how they arrive at results.

FaaF: Facts As A Function For Evaluating RAG

19 implied HN points • 04 Apr 24
🕹 Technology AI NLP Data Software Programming
  1. RAG systems often struggle to verify facts in generated text. This is because they don't focus enough on assessing the truthfulness of low-quality outputs.
  2. Verifying facts one by one takes a lot of time and resources. It's challenging to check multiple facts in a single generated response efficiently.
  3. The FaaF framework improves fact verification greatly. It simplifies the process, makes it more accurate, and cuts down the time needed for checking facts.

DRAGIN: Dynamic RAG Based On Real-Time Information Needs Of LLMs

19 implied HN points • 26 Mar 24
🕹 Technology AI Machine Learning Natural Language Data science Software Development
  1. Dynamic Retrieval Augmented Generation (RAG) improves the way information is retrieved and used in large language models during text generation. It focuses on knowing exactly when and what to look up.
  2. Traditional RAG methods often use fixed rules and may only look at the most recent parts of a conversation. This can lead to missed information and unnecessary searches.
  3. The new framework called DRAGIN aims to make data retrieval smarter and faster without needing further training of the language models, making it easy to use.
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Prompt-RAG

19 implied HN points • 20 Mar 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Computing
  1. Prompt-RAG is a new method that improves language models without using complex vector embeddings. It simplifies how we retrieve information to answer questions.
  2. The process involves creating a Table of Contents from documents, selecting relevant headings, and generating responses by injecting context into prompts. It makes handling data easier.
  3. While this method is great for smaller projects and specific needs, it still requires careful planning when constructing the documents and managing costs related to token usage.

Performing Multiple LLM Calls & Voting On The Best Result Are Subject To Scaling Laws

19 implied HN points • 19 Mar 24
🕹 Technology AI Machine Learning Data science Systems Design Performance optimization
  1. Making more calls to Large Language Models (LLMs) can help with simple questions but may actually make it harder to answer tough ones.
  2. Finding the right number of calls to use is crucial for getting the best results from LLMs in different tasks.
  3. It's important to design AI systems carefully, as just increasing the number of calls doesn't always mean better performance.

TinyLlama Is An Open-Source Small Language Model

19 implied HN points • 15 Mar 24
🕹 Technology AI Software Open Source Mobile Language Models
  1. TinyLlama is a small but powerful language model that's open-source. It can be used on mobile devices and is great for trying out new ideas in language processing.
  2. This model is trained on a huge amount of text, around 1 trillion tokens, which helps it do a good job with various tasks. It performs better than other similar models.
  3. TinyLlama aims to keep getting better and more useful by adding new features and improving its performance in different applications.

LLMs Training SLMs

19 implied HN points • 12 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Model Training
  1. Orca-2 is designed to be a small language model that can think and reason by breaking down problems step-by-step. This makes it easier to understand and explain its thought process.
  2. The training data for Orca-2 is created by a larger language model, focusing on specific strategies for different tasks. This helps the model learn to choose the best approach for various challenges.
  3. A technique called Prompt Erasure helps Orca-2 not just mimic larger models but also develop its own reasoning strategies. This way, it learns to think cautiously without relying on direct instructions.

Self-Reflective Retrieval-Augmented Generation (SELF-RAG)

19 implied HN points • 04 Mar 24
🕹 Technology AI Machine Learning Generative AI Language Models Data science
  1. SELF-RAG is designed to improve the quality and accuracy of responses from generative AI by allowing the AI to reflect on its own outputs and decide if it needs to retrieve additional information.
  2. The process involves generating special tokens that help the AI evaluate its answers and determine whether to get more information or stick with its original response.
  3. Balancing efficiency and accuracy is crucial; too much focus on speed can lead to wrong answers, while aiming for perfect accuracy can slow down the system.

Catastrophic Forgetting In LLMs

19 implied HN points • 22 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Natural Language Processing
  1. Catastrophic forgetting happens when language models forget things they learned before as they learn new information. It's like a student who forgets old lessons when they study new subjects.
  2. Language models can change their performance over time, sometimes getting worse instead of better. This means they can produce different answers for the same question at different times.
  3. Continuous training can make models forget important knowledge, especially in understanding complex topics. Researchers suggest that special training techniques might help reduce this forgetting.

Five Stages Of LLM Implementation [Updated]

19 implied HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data Strategy
  1. Large Language Models (LLMs) have improved how AI systems understand and talk to people. Companies need to focus on a solid data strategy to use AI successfully.
  2. Implementing LLMs can be tricky because they often rely on external APIs. Having local models can solve many operational challenges, but requires technical skills.
  3. Different stages of LLM development include assisting in chatbot design, refining responses, and using advanced techniques like Document Search, which improves how chatbots retrieve and use information during conversations.

Demonstrate, Search, Predict (DSP) for LLMs

19 implied HN points • 16 Feb 24
🕹 Technology AI NLP Machine Learning Data science Software Development
  1. The Demonstrate, Search, Predict (DSP) approach is a method for answering questions using large language models by breaking it down into three stages: demonstration, searching for information, and predicting an answer.
  2. This method improves efficiency by allowing for complex systems to be built using pre-trained parts and straightforward language instructions. It simplifies AI development and speeds up the creation of new systems.
  3. Decomposing queries, known as Multi-Hop or Chain-of-Thought, helps the model reason through questions step by step to arrive at accurate answers.

T-RAG = RAG + Fine-Tuning + Entity Detection

19 implied HN points • 15 Feb 24
🕹 Technology AI LLMs Data Privacy Software Development Machine Learning
  1. T-RAG is a method that combines RAG architecture with fine-tuned language models and an entity detection system for better information retrieval. This approach helps in answering questions more accurately by focusing on relevant context.
  2. Data privacy is crucial when using language models for sensitive documents, so it's better to use open-source models that can be hosted on-premise instead of public APIs. This helps prevent any risk of leaking private information.
  3. The model uses an entities tree to improve context when processing queries, ensuring relevant entity information is included in the responses. This makes the answers more useful and comprehensive for the user.

Craft Successful Conversational User Interfaces: Align User Intent With Developed Intent

19 implied HN points • 08 Feb 24
🕹 Technology AI User Interfaces Chatbots Natural Language Digital Tools
  1. It's important to match what users want to talk about with what the chatbot is set up to respond to. This makes conversations smoother and more enjoyable.
  2. Understanding different user intents helps in designing better chatbot interactions. Analyzing common questions can improve how the chatbot replies.
  3. Chatbots should be regularly updated based on user behavior and feedback. This helps keep the chatbot relevant and able to meet changing needs.

A Benchmark for Verifying Chain-Of-Thought

19 implied HN points • 07 Feb 24
🕹 Technology AI Data Research Machine Learning Verification
  1. A new dataset called REVEAL helps check if reasoning used in answers is correct or logical. It assesses whether each part of the reasoning leads to the final answer.
  2. REVEAL focuses on verifying claims based on provided evidence. It does not check how the evidence was found, but how well the reasoning uses it.
  3. Creating detailed datasets like REVEAL is complex and time-consuming. It requires skilled annotators to carefully evaluate the logic and relevance in each reasoning step.

Corrective RAG (CRAG)

19 implied HN points • 05 Feb 24
🕹 Technology AI Data Machine Learning Information Retrieval Software Development
  1. Corrective Retrieval Augmented Generation (CRAG) helps improve how data is used in language models by correcting errors from retrieved information.
  2. It uses a special tool called a retrieval evaluator to check the quality of the data and decide if it's correct, incorrect, or unclear.
  3. CRAG is designed to work well with different systems, making it easier to apply in various situations while enhancing document use.

Adding Noise Improves RAG Performance

19 implied HN points • 02 Feb 24
🕹 Technology AI Research Information Retrieval Machine Learning Language Models Data Analysis
  1. Adding irrelevant documents can actually improve accuracy in Retrieval-Augmented Generation systems. This goes against the common belief that only relevant documents are useful.
  2. In some cases, having unrelated information can help the model find the right answer, even better than using only related documents.
  3. It's important to carefully place both relevant and irrelevant documents when building RAG systems to make them work more effectively.

MultiHop-RAG

19 implied HN points • 31 Jan 24
🕹 Technology AI Machine Learning Data science Software Development Language Models
  1. Multi-hop retrieval-augmented generation (RAG) helps answer complex questions by pulling information from multiple sources. It connects different pieces of data to create a clear and complete answer.
  2. Using a data-centric approach is becoming more important for improving large language models (LLMs). This means focusing on the quality and relevance of the data to enhance how models learn and generate responses.
  3. The development of prompt pipelines in RAG systems is gaining attention. These pipelines help organize the process of retrieving and combining information, making it easier for models to handle text-related tasks.

Visualise & Discover RAG Data

19 implied HN points • 23 Jan 24
🕹 Technology AI Data science Software Development Machine Learning Data Visualization
  1. RAGxplorer is a tool that helps visualize and explore data chunks, making it easier to understand how they relate to different topics.
  2. The process of Retrieval-Augmented Generation (RAG) involves breaking documents into smaller chunks to improve how data is retrieved and used with language models.
  3. Visualizing data can help identify problems like missing information or unexpected results, allowing users to refine their questions or understand their data better.

LangSmith by LangChain

19 implied HN points • 22 Jan 24
🕹 Technology AI Tools Software Development Data science Machine Learning Programming Languages
  1. LangSmith helps organize and manage projects and data for applications built with LangChain. It allows you to see your tasks in a neat layout and check performance easily.
  2. The platform offers tools for testing and improving agents, especially when handling multiple tasks at the same time. This helps ensure that applications run smoothly.
  3. LangSmith allows users to create datasets that can improve agent performance. It also has features to evaluate how well agents are doing by comparing their outputs to expected results.

Understanding LLM User Experience & Expectation

19 implied HN points • 18 Jan 24
🕹 Technology AI User Experience Research Natural Language
  1. Most users engage with LLMs weekly and mainly use them for tasks like getting information and solving problems. It's a popular tool that people find helpful.
  2. Users expect LLMs to perform well in creative tasks too, but many are not satisfied with the results they get in this area. There’s room for better performance here.
  3. Understanding what users want from LLMs is key. This includes recognizing their different needs, like trust and capability in the tools, so improvements can be better targeted.

What Is LangChain Expression Language (LCEL)?

19 implied HN points • 09 Jan 24
🕹 Technology AI Software Development Programming Applications
  1. LangChain Expression Language (LCEL) helps build applications using large language models. It simplifies the process of creating apps by breaking down components into a clear sequence.
  2. LCEL combines pro-code and low-code approaches, making it easier for developers to create reusable pieces of code. This can save time and help manage complexity in applications.
  3. With LCEL, you can run operations like invoking and batching in a structured way. This makes it easier to manage how different parts of an application work together.

Active Prompting with Chain-of-Thought for Large Language Models

19 implied HN points • 05 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Automation
  1. AI can help improve language models by using a four-step process: estimating uncertainty, selecting uncertain questions, annotating them, and making final inferences. This helps ensure better answers.
  2. Using human annotations along with AI makes the training data clearer and reduces confusion. It allows us to focus on the most important information for the models.
  3. Companies can benefit from this approach by streamlining how they handle data. It promotes a more organized way of discovering, designing, and developing data.

Cohere Coral

19 implied HN points • 23 Nov 23
🕹 Technology AI Chatbots User Interfaces APIs Data Privacy
  1. Cohere Coral is a chat interface that uses large language models and competes with others like ChatGPT. It's designed to be easy to use with no coding required.
  2. Coral can either answer questions based on its existing knowledge or look up information online for the latest answers. This helps provide accurate and timely responses.
  3. The tool allows businesses to customize its features and ensures that data stays private. It's a great option for companies looking to enhance their customer interaction.

Chain-Of-Knowledge Prompting

19 implied HN points • 22 Nov 23
🕹 Technology AI NLP Machine Learning Data science Natural Language
  1. Chain-Of-Knowledge (CoK) prompting is a useful technique for complex reasoning tasks. It helps make AI responses more accurate by using structured facts.
  2. Creating effective prompts using CoK requires careful construction of evidence and may involve human input. This is important for ensuring the quality and reliability of the information AI generates.
  3. The CoK approach aims to reduce errors or 'hallucinations' in AI responses. It offers a more transparent way to build prompts and enhances the overall reasoning ability of AI systems.

LangChain & LLM Based Autonomous Agents

39 implied HN points • 26 Apr 23
🕹 Technology AI Software Automation Data Applications
  1. Large Language Models (LLMs) can be programmed with reusable prompts. This helps in integrating them into bigger applications easily.
  2. Creating chains of interactions allows LLMs to work together in a structured way for more complex tasks.
  3. Agents can operate independently, using tools to find answers without being stuck to a fixed plan, making them more flexible.

How Should Large Language Models Be Evaluated?

19 implied HN points • 06 Nov 23
🕹 Technology AI Machine Learning Data science Natural Language Processing Evaluation
  1. When evaluating large language models (LLMs), it's important to define what you're trying to achieve. Know the problems you're solving so you can measure success and failure.
  2. Choosing the right data is crucial for evaluating LLMs. You'll need to think about what data to use and how it will be delivered in your application.
  3. The process of evaluation can be automated or involve human input. Deciding how to implement this process is key to building effective LLM applications.

Meta-In-Context Learning For Large Language Models (LLMs)

19 implied HN points • 24 Oct 23
🕹 Technology AI Machine Learning Language Models NLP Chatbots
  1. Meta-in-context learning helps large language models use examples during training without needing extra fine-tuning. This means they can get better at tasks just by seeing how to do them.
  2. Providing a few examples can improve how well these models learn in context. The more they see, the better they understand what to do.
  3. In real-world applications, it's important to balance quick responses and accuracy. Using the right amount of context quickly can enhance how well the model performs.

Updated: Emerging RAG & Prompt Engineering Architectures for LLMs

19 implied HN points • 18 Oct 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Software Development
  1. Large Language Models (LLMs) rely on both input and output data that are unstructured and conversational. This means they process language in a natural, free-flowing manner.
  2. Fine-tuning LLMs has become less popular because it requires a lot of specific training and can get outdated. Using contextual prompts at the right time is a better way to improve their accuracy.
  3. New tools are emerging that test different LLMs against prompts instead of just tweaking prompts for one LLM. This helps in finding the best model suited for different tasks.

ChatGPT Models, Structure & Input Formats

19 implied HN points • 11 Apr 23
🕹 Technology AI Chatbots APIs Data NLP
  1. ChatGPT is more than just a large language model; it's a conversational service that uses AI to manage conversations and gather data from different sources.
  2. Plugins allow ChatGPT to connect with other applications, making it more versatile and capable of performing various tasks, similar to apps in an app store.
  3. Using the ChatGPT API requires understanding specific formats for input and output, which helps in building custom applications with the AI.

Prompt Chaining

19 implied HN points • 06 Apr 23
🕹 Technology AI Programming Chatbots User Interface Data Transformation
  1. Visual Programming tools are being used to connect prompts in applications, making it easier to create conversational interfaces.
  2. Chaining prompts involves transforming and organizing data from responses to ensure better output and decision-making in AI applications.
  3. Good design of these tools includes making it easy to build, edit, and debug chains while also allowing users to interact flexibly with the AI.

These Are The Challenges When Creating A LLM Based Conversational Interface

19 implied HN points • 01 Mar 23
🕹 Technology AI LLMs Chatbots NLP Applications
  1. Creating conversational interfaces with language learning models (LLMs) is tricky because the responses can be very different each time. This makes it hard to keep conversations flowing smoothly.
  2. If you change something small in the middle of a conversation, it can mess up everything that comes after. This makes planning the conversation a bit complicated.
  3. As these chatbots get more complex, we can use groups of connected steps to manage the conversation better. Future tools might make it easier for people to design these conversations without coding.

The Cobus Quadrant™ Of Conversation Design Capabilities

19 implied HN points • 09 Feb 23
🕹 Technology AI Design Development Platforms
  1. Conversation design focuses on creating the flow of dialogue between users and AI. It’s about defining how users interact and how responses are managed.
  2. Different AI platforms offer unique features for conversation design, like dialog flow management or visual design tools. Some tools are easier to use while others provide more advanced capabilities.
  3. Keeping the user experience in mind is crucial for successful conversation design. Understanding what the user needs helps to create smoother and more effective interactions.

Large Language Model Use & Augmentation

0 implied HN points • 24 Jul 24
🕹 Technology AI NLP Machine Learning Chatbots Voice Assistants
  1. Large Language Models (LLMs) like GPT-3 have opened up new possibilities for applications, but they also have significant limitations. These include not being able to remember past conversations and giving different answers to the same question.
  2. LLMs can produce incorrect or misleading information, a phenomenon known as 'hallucinations'. This can be a challenge, especially when accuracy is needed, but certain strategies can help improve their responses.
  3. AI agents built on LLMs can perform specific tasks by using tools and making decisions. This makes them useful in various applications, like answering questions or managing purchases.

LangGraph Introduced SubGraphs

0 implied HN points • 30 Jul 24
🕹 Technology Artificial Intelligence Software Development UI/UX Design Machine Learning Data Management
  1. LangGraph allows users to create and manage states using graphs. This helps in making complex conversation flows simpler and more organized.
  2. Sub-graphs can perform specific tasks like summarizing logs separately while still connecting back to a main graph. This lets each section work independently but share important information.
  3. LangGraph is flexible and lets users visualize and modify conversation flows easily. It works with regular Python functions, making it adaptable for various applications.

AgentInstruct Uses Agentic Flows To Create Synthetic Training Data

0 implied HN points • 16 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Automation
  1. Microsoft is using advanced methods to create high-quality synthetic training data for language models. This helps improve the data's diversity and reduces the need for human oversight.
  2. Agentic workflows are important because they allow multiple agents to generate and refine data, making the process more efficient and effective.
  3. The approach can create large amounts of customized data from unstructured sources quickly, which is useful for enhancing AI models during different training stages.

LLM-Driven Synthetic Data Generation, Curation & Evaluation

0 implied HN points • 02 Aug 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Synthetic Data
  1. Human oversight is key when generating synthetic data. It helps catch mistakes and ensure the data is useful for training models.
  2. Data quality and variety matter a lot in training language models. The better the data design, the better the model learns and performs.
  3. A solid structure for data creation can improve the efficiency and accuracy of generating synthetic data. This makes it more relevant to real-world applications.

LLM Hallucination Index

0 implied HN points • 16 Nov 23
🕹 Technology AI Machine Learning Data NLP Automation
  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.

AI Agents

0 implied HN points • 06 Aug 24
🕹 Technology Artificial Intelligence Software Development Machine Learning User Interfaces Automation
  1. AI Agents are programs that use large language models to work on tasks independently. They can break down complex questions and find solutions like humans do.
  2. These agents can handle tasks by analyzing user interfaces and predicting next actions by looking at icons and text. This makes them more effective in completing tasks on screens.
  3. Recent advancements have improved AI Agents' ability to understand and navigate user interfaces, allowing them to act more like real users. This helps them give better and more accurate results.