The hottest NLP Substack posts right now

And their main takeaways
Category
Top Technology Topics

Large Language Model (LLM) Disruption of Chatbots

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Oct 23
🕹 Technology NLP
  1. Large Language Models (LLMs) are changing the way chatbots are built. They can help improve understanding of what users say by grouping similar questions and making designs easier.
  2. Voice technology is becoming more important in customer support, leading to more complex conversations. This includes using voice recognition and speech synthesis to help handle customer queries.
  3. There are ongoing challenges with trust and privacy when using LLMs. Companies need to make sure they protect personal information while also proving they are using the technology responsibly.

Large Language Model (LLM) Stack — Version 5

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Oct 23
🕹 Technology NLP
  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.

LangSmith Hub By The Numbers

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Oct 23
🕹 Technology NLP
  1. LangSmith has four main parts: Projects, Data, Testing, and Hub. The first three are all about improving production, while Hub is for testing before launch.
  2. Chatbots are the most popular use case for using large language models, followed closely by summarization and questions and answers on documents.
  3. OpenAI leads the prompt count in the LangSmith Hub, followed by Anthropic and Google. This shows how important different models are when experimenting with prompts.

Large Language Model Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Oct 23
🕹 Technology NLP
  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 NLP
  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.
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Fine-Tuning LLMs With Retrieval Augmented Generation (RAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Oct 23
🕹 Technology NLP
  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 NLP
  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.

Automatic Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Sep 23
🕹 Technology NLP
  1. Automatic Prompt Engineering (APE) creates prompts for text generation based on what you want as input and output. It helps make the process easier and faster.
  2. With APE, a computer can suggest the best prompts by testing different options and scoring them for quality. This reduces the need for a human to write every prompt manually.
  3. Using APE allows for better interaction with large language models by focusing on user intent and context. It makes conversations feel more natural and responsive.

What Is The Future Of Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Sep 23
🕹 Technology NLP
  1. Prompt engineering might not be the main way we interact with AI in the future. It seems we'll use more natural and voice-based communication instead.
  2. Understanding context and reducing ambiguity are key challenges in human-like conversations with AI. This helps the AI to provide better answers.
  3. For businesses, fine-tuning models and using tools like context references help improve AI responses. Both methods work together to make AI better.

Chain-Of-Thought Prompting In LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Apr 23
🕹 Technology NLP
  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.

OpenAI Playground Start & Restart Prompt Injection

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Apr 23
🕹 Technology NLP
  1. OpenAI is using ChatML to help the AI tell the difference between human and machine text. This can reduce bad prompt injections by recognizing who is giving instructions.
  2. They have introduced different modes for specific tasks. Each mode has its own setup to guide users on how to interact with the AI effectively.
  3. New options in OpenAI Playground let users add text at the beginning or end of an AI response. This helps create better conversations and reminds users how to make good prompts.

Scaleable Prompt Pipelines For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 18 Apr 23
🕹 Technology NLP
  1. Creating good prompts for AI needs context. A well-structured prompt includes clear instructions, context, the user's question, and the expected answer format.
  2. To handle many prompts at once, automation is key. Using tools to automatically search and retrieve the right context for prompts will save time and improve responses.
  3. For AI to work well in specific areas, it needs accurate and well-organized data. This data helps improve the AI’s answers, especially in narrow topics.

The Evolution Of Prompt Engineering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Apr 23
🕹 Technology NLP
  1. Prompt engineering is important for getting the best responses from large language models. Users have to carefully design prompts to mimic what they want the model to generate.
  2. Static prompts can be turned into templates with placeholders that can be filled in later. This makes it easier to reuse and share prompts in different situations.
  3. Prompt pipelines allow users to create more complex applications by linking several prompts together. This helps organize how information is processed and improves user interaction with chatbots.

The Foundation Conversational AI Technologies: Version 3

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Apr 23
🕹 Technology NLP
  1. There's been a rise in chatbot development frameworks that now include large language models (LLMs). This means chatbots can do more complex tasks than before.
  2. LLMs are not just for generating responses anymore. They can help create entire conversation flows and assist developers more effectively.
  3. Future improvements will focus on better fine-tuning and supervision methods for LLMs, making them even smarter and more useful.

5 Things NLU Engines Are Really Good At

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Apr 23
🕹 Technology NLP
  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.

Prompt Engineering, OpenAI & Modes

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Mar 23
🕹 Technology NLP
  1. Prompt engineering is really important for getting the most out of large language models. Good prompts can help the model give accurate and relevant responses.
  2. To prevent models from making things up or 'hallucinating,' prompts need to be carefully structured and put together. This helps keep the context clear and the information reliable.
  3. OpenAI is working on improving the safety and quality of responses using better prompt structures. This reduces risks like prompt injection attacks and helps ensure more consistent answers.

Large Language Models, Foundation Models & Multi-Modal Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Mar 23
🕹 Technology NLP
  1. Large Language Models (LLMs) are being developed into Foundation Models that can handle tasks beyond just language, like images and voice. This shows how technology is evolving to be more versatile.
  2. GPT-4 is now seen as a Multi-Modal Model that combines different types of data, allowing it to work with text, images, and more. This expands the possibilities for AI applications.
  3. As the use of LLMs increases, there will be more focus on creating fine-tuned models. This means turning unstructured data into structured data for better interaction and understanding.

Adding A Moderation Layer To Your OpenAI Implementations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Mar 23
🕹 Technology NLP
  1. Adding a moderation layer to OpenAI implementations is essential to comply with usage policies. This helps avoid serious issues like account termination.
  2. The moderation endpoint is free to use and monitors for harmful content like hate, violence, and self-harm. Companies should check their API calls for inappropriate content.
  3. OpenAI is continually improving the moderation tools, but users need to frequently update their own policies to align with these changes. Regular checks can help ensure safe usage.

NLU & NLG Should Go Hand-In-Hand

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Mar 23
🕹 Technology NLP
  1. Using NLU and NLG together can make chatbots work better. They can detect what users want and give accurate responses.
  2. Traditional NLU systems still have strong abilities in understanding user intent that shouldn’t be ignored. They're a valuable asset in chatbot design.
  3. Regularly checking and updating the prompts used by chatbots can help improve how they respond to users, making interactions more effective.

When Using The ChatGPT API, Users Will Have To Manage The Context

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Mar 23
🕹 Technology NLP
  1. When using the ChatGPT API, users must provide context for the conversation because it doesn't remember past interactions. You need to include previous messages to keep the conversation clear.
  2. If the number of messages exceeds a limit, you can keep only the most recent ones to save space. This way, the model still understands the flow of the conversation.
  3. If you want better responses, you should be clear with your instructions and specify what type of answer you need. Changing how you ask questions can help improve the output.

The Introduction Of Chat Markup Language (ChatML) Is Important For A Number Of Reasons

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Mar 23
🕹 Technology NLP
  1. Chat Markup Language (ChatML) helps improve security for large language models by protecting against prompt injection attacks. This means it can make conversations safer and more reliable.
  2. ChatML organizes conversations into roles like system, assistant, and user. This helps clarify who is saying what in the conversation, which can reduce misunderstandings.
  3. The development of ChatML is just starting, and future updates will likely allow it to handle more than just text. It may soon include images, sound, and other data types, making it even more versatile.

Foundation Conversational AI Technologies Landscape

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Feb 23
🕹 Technology NLP
  1. The conversational AI field is quickly evolving in three main areas: voicebots, agent assistance, and large language model (LLM) enablement.
  2. Many current AI systems focus on generating responses, but there's a missed opportunity to use predictive features effectively.
  3. Traditional natural language understanding systems still perform better in terms of cost and training compared to LLMs, especially for certain tasks.

Existing Rigid Chatbot Architecture Needs Large Language Model (LLM) Flexibility

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Feb 23
🕹 Technology NLP
  1. Current chatbot systems are too rigid and are mostly based on fixed rules and flows. They can't adapt easily to different conversations, making them less effective.
  2. Large language models (LLMs) have the potential to make chatbots more flexible and smarter. They can help chatbots understand and respond to a wider range of user inputs.
  3. Innovative new frameworks for conversational AI are emerging. These allow for more personalized interactions by combining different components based on user needs.

Bootstrapping A Chatbot With A Large Language Model

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Feb 23
🕹 Technology NLP
  1. To build a chatbot, you can organize unstructured data by clustering it into themes called intents. This helps make sense of lots of information and sets the stage for training the bot.
  2. Once the bot receives a user's message, it uses semantic search to match the message with the right intent. This helps in retrieving the most relevant information quickly.
  3. The bot then generates a response using the matched intent and the user's question. This process allows the chatbot to provide accurate and context-aware answers.

Foundation Conversational AI Technologies

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 10 Feb 23
🕹 Technology NLP
  1. Conversational AI (CAI) technologies are grouped by their areas, but sometimes it's tricky to fit them into just one category. Many technologies overlap.
  2. The focus is mainly on foundational technologies instead of specific products or solutions, which are too numerous to cover in detail.
  3. Feedback and suggestions for improvement are encouraged to make future versions better.

The Cobus Quadrant™ Of NLU Design

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Feb 23
🕹 Technology NLP
  1. Understanding customer intent is key to making chatbots work well. Starting with what customers want helps create better and more trusted AI experiences.
  2. NLU Design is about turning messy data into clear information for chatbots. It involves organizing unstructured data and using both human input and machine help to label and manage it.
  3. Improving chatbots requires ongoing evaluation and fine-tuning. Regularly checking their performance and making adjustments helps keep them responsive to users' needs.

NLP/NLU, Chatbots, Conversational UI/UX, Developer, Ubiquitous User Interfaces

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Jun 21
🕹 Technology NLP
  1. NLP and NLU help machines understand human language better. This makes chatbots and voicebots more effective in conversations.
  2. Conversational UI/UX focuses on making user interactions with technology feel natural and engaging. Good design improves user satisfaction.
  3. Developers play a key role in building these technologies. Their skills help create seamless and intuitive interfaces for users.

Dissecting the developer strategies of 3 leading AI startups

Router by Dmitry Pimenov • 0 implied HN points • 16 Mar 23
🕹 Technology NLP
  1. Diffusion models are making waves in generative AI, allowing for creative image manipulation by removing noise from images. This technology has opened doors for tools that can create high-quality images from simple text prompts.
  2. Large Language Models like ChatGPT are changing the way we interact with technology. They utilize vast amounts of text data to provide smart and coherent answers to complex questions, sparking a competitive race among tech giants to develop their own AI solutions.
  3. Having a solid API strategy is crucial for AI startups. Companies like OpenAI, Hugging Face, and Speechly show that understanding user needs and creating easy-to-use interfaces can lead to success in the rapidly evolving AI landscape.

[in case you missed it] Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 21
🕹 Technology NLP
  1. AI is improving tools like Google Maps, making them smarter and more helpful with real-time updates.
  2. It's important to focus on building effective machine learning systems that provide real value, instead of just labeling everything as AI.
  3. Data can be powerful for decision-making, but relying too heavily on numbers can lead to mistakes and misinterpretation.

[in case you missed it] Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 0 implied HN points • 06 Sep 20
🕹 Technology NLP
  1. A new machine learning algorithm helped identify 50 new planets by analyzing old NASA data. This shows how AI can unlock discoveries from existing information.
  2. There has been a significant drop in deep learning job postings recently, especially among smaller companies. This indicates a shift in the demand for deep learning talent after the pandemic.
  3. Apple has launched a residency program for people with STEM backgrounds to improve their machine learning skills. This offers participants hands-on experience and personalized training.

[in case you missed it] Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 0 implied HN points • 09 Aug 20
🕹 Technology NLP
  1. GPT-3 can create very human-like text and it can even write computer programs with just a few examples. This shows how advanced AI language models are becoming.
  2. Many languages are spoken around the world, but most natural language processing work has focused only on English. It's important to include other languages in research.
  3. Graph technologies are being used to solve complex business problems, such as making recommendations and detecting fraud. They are becoming essential tools in data science.

[in case you missed it] Data Science Weekly - Issue 349

Data Science Weekly Newsletter • 0 implied HN points • 02 Aug 20
🕹 Technology NLP
  1. Deep learning has important historical ideas that everyone in the field should know. Learning these basics can help new learners understand current research.
  2. As technology like GPT-3 emerges, understanding the hype around it is key. It helps to have a framework for sorting through the excitement and noise.
  3. There are challenges in using machine learning in production. It's easy to create a simple model, but making it work well with changing data is much harder.

[in case you missed it] Data Science Weekly - Issue 328

Data Science Weekly Newsletter • 0 implied HN points • 08 Mar 20
🕹 Technology NLP
  1. Neuroscience is struggling to create clear theories about how the brain works, which makes finding the right path forward challenging. It's important to understand that simply collecting data isn't enough to advance our knowledge.
  2. There are many resources out there trying to simplify machine learning concepts for everyone. These aim to provide real-world examples and easy-to-understand explanations, making it accessible for all types of learners.
  3. Self-supervised learning is making a significant impact in both language processing and computer vision fields. This approach allows models to learn from data without needing extensive labeled examples, which can be a game changer.

[in case you missed it] Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 0 implied HN points • 20 Jul 19
🕹 Technology NLP
  1. Netflix is moving away from collaborative filtering for recommendations, focusing on more effective strategies that drive revenue.
  2. Machine learning can play a big role in tackling climate change, helping us find solutions to one of our biggest challenges.
  3. There is a growing demand for data scientists to know a variety of tools like Python, R, and SQL, so it's important to keep learning and improving your skills.

Generative A-Eye #14 - 8th Oct,2024

Martin’s Newsletter • 0 implied HN points • 08 Oct 24
🕹 Technology NLP
  1. There are new methods in AI for creating 3D clothing try-ons that use something called Gaussian Splatting. This could change how we shop for clothes online.
  2. Researchers are finding new ways to improve deepfake detection, which helps identify fake images and videos. This is important for keeping information trustworthy.
  3. A technique called AutoLoRA helps make AI models create better quality images while also maintaining diversity. This could lead to more creative and interesting results in image generation.

Embedding-Based Tool Selection for AI Agents

Bit Byte Bit • 0 implied HN points • 21 Dec 25
🕹 Technology NLP
  1. Embed tool descriptions and use semantic search to pick the top few relevant tools per query so you dramatically cut token usage and improve the model's tool‑selection accuracy.
  2. Choose an embedding provider based on your needs — calling OpenAI is simple and cheap for small volumes, while running a local model gives privacy and low latency but adds operational overhead — and hide that choice behind a provider abstraction so you can swap easily.
  3. Pure similarity can miss multi‑step dependencies, so expand selections by category and tune your similarity threshold, have a cold‑start fallback, and you'll get big wins in cost and latency.