The hottest NLP Substack posts right now

And their main takeaways
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Top Technology Topics

LangChain Based Plan & Execute AI Agent With GPT-4o-mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 99 implied HN points • 26 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Programming
  1. The Plan-and-Solve method helps break tasks into smaller steps before executing them. This makes it easier to handle complex jobs.
  2. Chain-of-Thought prompting can sometimes fail due to calculation errors and misunderstandings, but newer methods like Plan-and-Solve are designed to fix these issues.
  3. A LangChain program allows you to create an AI agent to help plan and execute tasks efficiently using the GPT-4o-mini model.

OpenAI Enhanced Their API With Robust Structured Output Capabilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 12 Aug 24
🕹 Technology AI API Data Development NLP
  1. OpenAI has improved its API to ensure that outputs always match a set JSON format. This helps developers know exactly what kind of data they will get back.
  2. The previous method of generating JSON outputs was inconsistent, making it hard to use in real-world applications. Now, there's a more reliable way to create structured outputs.
  3. Developers can now use features like Function Calling and a new response format to make their apps interact better with AI, ensuring clearer communication between systems.

Unveiling the Revolutionary Architecture behind LLMs - "Attention is all you need"

Mindful Matrix • 219 implied HN points • 17 Mar 24
🕹 Technology AI NLP Architecture Training Applications
  1. The Transformer model, introduced in the groundbreaking paper 'Attention Is All You Need,' has revolutionized the world of language AI by enabling Large Language Models (LLMs) and facilitating advanced Natural Language Processing (NLP) tasks.
  2. Before the Transformer model, recurrent neural networks (RNNs) were commonly used for language models, but they struggled with modeling relationships between distant words due to their sequential processing nature and short-term memory limitations.
  3. The Transformer architecture leverages self-attention to analyze word relationships in a sentence simultaneously, allowing it to capture semantic, grammatical, and contextual connections effectively. Multi-headed attention and scaled dot product mechanisms enable the Transformer to learn complex relationships, making it well-suited for tasks like text summarization.

AI Agents With Human In The Loop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 15 Aug 24
🕹 Technology AI Automation Chatbots NLP Frameworks
  1. AI agents can now include human input at important points, which helps make their actions safer and more reliable. This way, humans can step in when needed without taking over the whole process.
  2. LangGraph is a new tool that helps organize and manage how these AI agents work. It uses a graph approach to show steps and allows for better oversight and control.
  3. By combining automation with human checks, we can create more efficient systems that still have the safety of human involvement. This lets us enjoy the benefits of AI while also addressing concerns about its autonomy.

RAG Implementations Fail Due To Insufficient Focus On Question Intent

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 18 Jul 24
🕹 Technology AI NLP Data science Machine Learning Knowledge Graphs
  1. Large Language Models (LLMs) can create useful text but often struggle with specific knowledge-based questions. They need better ways to understand the question's intent.
  2. Retrieval-augmented generation (RAG) systems try to solve this by using extra knowledge from sources like knowledge graphs, but they still make many mistakes.
  3. The Mindful-RAG approach focuses on understanding the question's intent more clearly and finding the right context in knowledge graphs to improve answers.
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The Future of Prompt Engineering

Gradient Flow • 559 implied HN points • 04 May 23
🕹 Technology AI NLP Modeling Data Analysis Tools
  1. NLP pipelines are shifting to include large language models (LLMs) for accuracy and user-friendliness.
  2. Effective prompt engineering is crucial for crafting useful input prompts tailored to generative AI models.
  3. Future prompt engineering tools need to be interoperable, transparent, and capable of handling diverse data types for collaboration and model sharing.

Language Agent Tree Search — LATS

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 12 Jun 24
🕹 Technology AI NLP Automation Software Data
  1. The LATS framework helps create smarter agents that can reason and make decisions in different situations. It's designed to enhance how language models think and plan.
  2. Using external tools and feedback in the LATS framework makes agents better at solving complex problems. This means they can learn from past experiences and improve their responses over time.
  3. LATS allows agents to explore many possible actions and consider different options before making a choice. This flexibility leads to more thoughtful and helpful interactions.

Phi-3 Is A Small Language Model Which Can Run On Your Phone

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 19 Jun 24
🕹 Technology AI NLP Machine Learning Data science Software Development
  1. Phi-3 is a small language model that can run directly on your phone, making it accessible for local use instead of needing cloud connections. This means you can use it anywhere without relying on internet speed.
  2. Small language models like Phi-3 are good for specific tasks and regulated industries where data privacy is important. They can provide quick and accurate responses while keeping your data secure.
  3. Training for Phi-3 involves using high-quality data to improve its understanding of language and reasoning skills, allowing it to perform well on par with larger models, despite its smaller size.

TITAA #51: Tool People & People Tools

Things I Think Are Awesome • 157 implied HN points • 01 Feb 24
🕹 Technology AI VR Games NLP Tools
  1. Non-human tools with personality are becoming more common, especially with AI support.
  2. Large Language Models (LLMs) are being explored for creativity and role-playing, showing potential to improve creative output when working together.
  3. Real human behavior can sometimes view humans as disposable tools, with ongoing layoffs in industries like tech and games.

Building The Most Basic LangChain Chatbot

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 06 May 24
🕹 Technology AI Software Chatbots NLP Data
  1. Chatbots use Natural Language Understanding (NLU) to figure out what users want by detecting their intentions and important information.
  2. With Large Language Models (LLMs), chatbots can understand and respond to conversations more naturally, moving away from rigid, rule-based systems.
  3. Building a chatbot now involves using advanced techniques like retrieval-augmented generation (RAG) to pull in useful information and provide better answers.

GPT-4o mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 18 Jul 24
🕹 Technology AI NLP Machine Learning Software Development Data science
  1. GPT-4o mini is a new language model that's cheaper and faster than older models. It handles text and images and is great for tasks requiring quick responses.
  2. Small Language Models (SLMs) like GPT-4o mini can run efficiently on devices without relying on the cloud. This helps with costs, privacy, and gives users more control over the technology.
  3. SLMs are designed to be flexible and customizable. They can learn from various types of inputs and can adapt more easily to specific needs.

HILL: Solving for LLM Hallucination & Slop

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 23 May 24
🕹 Technology AI User Interface NLP Machine Learning Software Development
  1. HILL helps users see when large language models (LLMs) give wrong or misleading answers. It shows which parts of the response might be incorrect.
  2. The system includes different scores that rate the accuracy, credibility, and potential bias of the information. This helps users decide how much to trust the responses.
  3. Feedback from users helped shape HILL's features, making it easier for people to question LLM replies without feeling confused.

Evaluating The Quality Of RAG & Long-Context LLM Output

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 08 Jul 24
🕹 Technology AI NLP Machine Learning Data science Automation
  1. Evaluating the performance of RAG and long-context LLMs is tough because there isn't a common task to compare them on. This makes it hard to know which system works better.
  2. Salesforce created a new way to test these models called SummHay, where they summarize information from large text collections. The results show that even the best models struggle to match human performance.
  3. RAG systems generally do better at citing sources, while long-context LLMs might capture insights more thoroughly but have citation issues. Choosing between them involves trade-offs.

LangGraph Cloud

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

T5: Text-to-Text Transformers (Part One)

Deep (Learning) Focus • 157 implied HN points • 27 Mar 23
🕹 Technology Deep Learning NLP Model Training
  1. Transfer learning is powerful in deep learning, involving pre-training a model on one dataset then fine-tuning it on another for better performance.
  2. After BERT's breakthrough in NLP with transfer learning, T5 aims to analyze and unify various approaches that followed, improving effectiveness.
  3. T5 introduces a text-to-text framework for structuring tasks uniformly, simplifying how language tasks are converted to input-output text formats for models.

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

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

Data Science Weekly - Issue 499

Data Science Weekly Newsletter • 219 implied HN points • 16 Jun 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering NLP
  1. Using large language models can help kids learn to ask curious questions by automating the teaching process.
  2. New techniques for 3D space reconstruction can make indoor views on platforms like Google Maps look more realistic and interactive.
  3. There's a growing need to understand the value of personal data in online shopping, especially as new regulations come into play.

Creating A Benchmark Taxonomy For Prompt Engineering

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

TITAA #47: Authenticity and Control

Things I Think Are Awesome • 137 implied HN points • 30 Sep 23
🕹 Technology Art Gaming AI NLP
  1. The article discusses digital image tools that can augment daily lives, highlighting authenticity challenges.
  2. Issues with digital unreality in daily tools like image processing are becoming more evident and concerning.
  3. Advancements in AI algorithms are being used to create images that appear authentic, raising questions about what is real and what is artificially generated.

Using Fine-Tuning To Imbed Hidden Messages In Language Models

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

How Would The Architecture For An LLM Agent Platform Look?

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

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

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

Large Language Models vs Small Language Models: A Comparison

Rod’s Blog • 39 implied HN points • 20 Feb 24
🕹 Technology AI NLP Models Comparison
  1. Language models come in different sizes, architectures, training data, and capabilities.
  2. Large language models have billions or trillions of parameters, enabling them to be more complex and expressive.
  3. Small language models have less parameters, making them more efficient and easier to deploy, though they might be less versatile than large language models.

We Need Efficient and Transparent Language Models

Gradient Flow • 179 implied HN points • 01 Dec 22
🕹 Technology NLP Machine Learning Data Tools AI Reinforcement Learning
  1. Efficient and Transparent Language Models are needed in the field of Natural Language Processing for better understanding and improved performance.
  2. Selecting the right table format is crucial when migrating to a modern data warehouse or data lakehouse.
  3. DeepMind's work on controlling commercial HVAC facilities using reinforcement learning resulted in significant energy savings.

TITAA #41.5: Agents and Rich Description

Things I Think Are Awesome • 78 implied HN points • 15 Apr 23
🕹 Technology AI Games NLP Game design
  1. The post discusses Segment Anything for creative tasks, social agents in game contexts, and new LLMs in the AI landscape.
  2. The content covers AI art tools, game design elements like agents and NPCs, and updates in the field of NLP.
  3. The author mentions increases in paid subscriptions, interesting topics like AI art copyright, and shares a variety of exciting updates.

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

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

Step-Wise Controllable Agents From LlamaIndex

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

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

Demonstrate, Search, Predict (DSP) for LLMs

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

Embed Retrieve Win

Gradient Flow • 99 implied HN points • 29 Sep 22
🕹 Technology Machine Learning Data Infrastructure Generative models NLP AI Applications
  1. Embeddings are low-dimensional spaces that make AI applications faster and cheaper while maintaining quality.
  2. Vector databases are designed for vector embeddings and are becoming essential for modern search engines and recommendation systems.
  3. Generative models like diffusion models are gaining attention in the research community and offer great opportunities for exploration and innovative projects.

The Good A.I. We’re Not Talking About

The Digital Anthropologist • 19 implied HN points • 04 Jan 24
🕹 Technology AI ML NLP Deep Learning
  1. Artificial Intelligence (AI) is not just about Generative AI (GAI) like ChatGPT. There are various other proven AI tools like Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Expert Systems being successfully used in industries such as healthcare, manufacturing, and more.
  2. AI tools have been around for decades and have shown significant positive impacts on society. Despite the hype around GAI, it remains a small part of the broader AI landscape.
  3. Beyond the flashy headlines, many AI applications are working behind the scenes in specialized industries, quietly making a positive difference. While GAI is getting attention, the real-world impact of other AI tools continues to be substantial.

Sorry, But A.I. Doesn't Exist.

The Digital Anthropologist • 19 implied HN points • 09 Dec 23
🕹 Technology AI Machine Learning NLP Tools Generative AI
  1. Artificial Intelligence (AI) doesn't actually exist as a singular entity, but rather as a collection of various tools and technologies.
  2. While AI tools are important and valuable, they are currently limited to Narrow AI, meaning they excel at specific tasks but lack overall intelligence.
  3. Understanding the reality of AI, including its limitations and the motivations behind the hype, is crucial for regulation, governance, and innovation in the field.

Chain-Of-Knowledge Prompting

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

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

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

Gradient Flow #44: 2021 NLP Industry Survey Results; No-Code Landscape

Gradient Flow • 119 implied HN points • 23 Sep 21
🕹 Technology NLP No Code Machine Learning Data Tools Infrastructure
  1. The 2021 NLP Industry Survey received responses from 655 people worldwide, providing insights into how companies are using language applications today.
  2. Tools like Hugging Face NLP Datasets and TextDistance library are making data processing and comparison easier in Python.
  3. There is a trend towards low-code and no-code development tools that are boosting developer productivity and extending the pool of software application creators.