The hottest Natural Language Substack posts right now

And their main takeaways
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Top Technology Topics

The Weekly Kaitchup #65

The Kaitchup – AI on a Budget • 59 implied HN points • 01 Nov 24
🕹 Technology AI Models Machine Learning Natural Language Text-to-Speech Data science
  1. SmolLM2 offers alternatives to popular models like Qwen2.5 and Llama 3.2, showing good performance with various versions available.
  2. The Layer Skip method improves the speed and efficiency of Llama models by processing some layers selectively, making them faster without losing accuracy.
  3. MaskGCT is a new text-to-speech model that generates high-quality speech without needing text alignment, providing better results across different benchmarks.

Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts

Marcus on AI • 3952 implied HN points • 08 Dec 24
🕹 Technology AI Machine Learning Image Processing Natural Language Generative models
  1. Generative AI struggles with understanding complex relationships between objects in images. It sometimes produces physically impossible results or gets details wrong when asked to create images from text.
  2. Recent improvements in AI models, like DALL-E3, show only slight progress in handling specifications related to parts of objects. It can still mislabel parts or fail to follow more complex requests.
  3. AI systems need to improve their ability to check and confirm that generated images match the prompts given by users. This may require new technologies for better understanding between language and visuals.

o3: The grand finale of AI in 2024

Democratizing Automation • 815 implied HN points • 20 Dec 24
🕹 Technology AI Models Machine Learning Natural Language Software Development Research Trends
  1. OpenAI's new model, o3, is a significant improvement in AI reasoning. It will be available to the public in early 2025, and many experts believe it could change how we use AI.
  2. The o3 model has shown it can solve complex tasks better than previous models. This includes performing well on math and coding benchmarks, marking a big step for AI.
  3. As the costs of using AI decrease, we can expect to see these models used more widely, impacting jobs and industries in ways we might not yet fully understand.

The Sequence Knowledge # 555: Not All Benchmark are that Simple: An Intro to Multiturn Benchmarks

TheSequence • 14 implied HN points • 03 Jun 25
🕹 Technology AI Machine Learning Evaluation Natural Language Benchmarks
  1. Multi-turn benchmarks are important for testing AI because they make AIs more like real conversation partners. They help AIs keep track of what has already been said, making the chat more natural.
  2. These benchmarks are different from regular tests because they don’t just check if the AI can answer a question; they see if it can handle ongoing dialogue and adapt to new information.
  3. One big challenge for AIs is remembering details from previous chats. It's tough for them to keep everything consistent, but it's necessary for good performance in conversations.

AI Agents: Exploring Agentic Applications

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 119 implied HN points • 29 Jul 24
🕹 Technology AI Applications Machine Learning Natural Language Data Tools
  1. Agentic applications are AI systems that can perform tasks and make decisions on their own, using advanced models. They can adapt their actions based on user input and the environment.
  2. OpenAgents is a platform designed to help regular users interact with AI agents easily. It includes different types of agents for data analysis, web browsing, and integrating daily tools.
  3. For these AI agents to work well, they need to be user-friendly, quick, and handle mistakes gracefully. This is important to ensure that everyone can use them, not just tech experts.
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AI Writing is Morally Superior

From the New World • 75 implied HN points • 05 Dec 24
🕹 Technology AI writing Machine Learning Natural Language Content creation Media Ethics
  1. AI writing is changing the landscape of writing by making it more accessible. This means more people can share their ideas without needing the same level of skill as traditional writers.
  2. The criticism against AI writing often comes from writers who feel threatened. They think that AI takes away the uniqueness of human style, but many believe it actually helps get good ideas out to more people.
  3. AI can help present complex ideas in simpler ways. This could be beneficial, allowing more people to understand important truths that might be lost in fancy language.

AGI won't use computations

John Ball inside AI • 79 implied HN points • 23 Jun 24
🕹 Technology AI Computing Natural Language
  1. Artificial General Intelligence (AGI) might be achieved by focusing on pattern matching rather than traditional computations. This means understanding and recognizing complex patterns, just like how our brains work.
  2. Current AI systems struggle with tasks like driving or conversing naturally because they don't operate like human brains. Instead of tightly-coupled algorithms, more flexible and efficient pattern-based systems might be the key.
  3. Patom theory suggests that brains store and match patterns in a unique way, which allows for better learning and error correction. By applying these ideas, we could improve AI systems to be more human-like in understanding and interaction.

Languages don't need many words

John Ball inside AI • 39 implied HN points • 24 Jul 24
🕹 Technology Machine Learning Natural Language Artificial Intelligence Data science Speech Recognition
  1. You don't need many words to communicate in a new language. Just a small vocabulary can help you get by in everyday conversations.
  2. For understanding most spoken and written text, around 2000 words are usually enough. This covers about 80% of regular communication.
  3. Machine learning and AI can benefit from understanding language like humans do, by learning new words in context rather than just relying on a large vocabulary.

Teaching Small Language Models to Reason

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 10 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Using Chain-Of-Thought prompting helps large language models think through problems step by step, which makes them more accurate in their answers.
  2. Smaller language models struggle with Chain-Of-Thought prompting and often get confused because they don't have enough knowledge and understanding like the bigger models.
  3. Google Research has a method to teach smaller models by learning from larger ones. This involves using the bigger models to create helpful examples that the smaller models can then learn from.

RAG Survey & Available Research

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 27 Jun 24
🕹 Technology AI Machine Learning Natural Language Data science Deep Learning
  1. Retrieval-Augmented Generation (RAG) mixes retrieval methods with learning systems to help large language models use real-time data.
  2. RAG can enhance the accuracy of language models by incorporating current information, avoiding wrong answers that might come from outdated knowledge.
  3. The framework of RAG includes steps like pre-retrieval, retrieval, post-retrieval, and generation, each contributing to better outputs in language processing tasks.

TinyStories

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 26 Jun 24
🕹 Technology AI Machine Learning Natural Language Data science User Experience
  1. Phi-3 is a small language model that uses a special dataset called TinyStories. This dataset was designed to help the model create more varied and engaging stories.
  2. TinyStories uses simple vocabulary suitable for young children, focusing on quality over quantity. The stories generated are meant to be both understandable and entertaining.
  3. Training the Phi-3 model with TinyStories can be done quickly and allows for easier fine-tuning. This helps smaller organizations use advanced language models without needing huge resources.

Inside AI

John Ball inside AI • 39 implied HN points • 12 Jun 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data processing
  1. AGI might not come from current machine learning methods. Instead, understanding how human brains work could be the key to achieving it.
  2. The theory behind brain functions can help solve AI challenges. Learning from how brains process information could lead us to better AI solutions.
  3. Language is crucial for interacting with AI. Building a trustworthy AI community focused on language can improve how we communicate and use technology.

The Sequence Chat: Thinking About Transformers as Computers

TheSequence • 105 implied HN points • 30 Oct 24
🕹 Technology Artificial Intelligence Computing Machine Learning Natural Language Data science
  1. Transformers are changing AI, especially in how we understand and use language. They're not just tools; they act more like computers in some ways.
  2. The way transformers can adapt and scale is really impressive. It's like they can learn and adjust in ways traditional computers can't.
  3. Thinking of transformers as computers opens up new ideas about how we approach AI. This perspective can help us find new applications and improve our understanding of tech.

Moving From Natural Language Understanding To Mobile UI Understanding

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 11 Jul 24
🕹 Technology AI Machine Learning User Interface Natural Language
  1. Natural Language Understanding (NLU) helps machines grasp and respond to human language, making sense of unstructured conversations.
  2. The shift to Mobile UI Understanding means we are now focused on understanding what's on mobile screens instead of just conversations.
  3. The Ferret-UI model enables devices to interact with users in a more meaningful way, allowing for richer and more context-aware conversations.

The Sequence Chat: Why are Foundation Models so Hard to Explain and What are we Doing About it?

TheSequence • 77 implied HN points • 27 Nov 24
🕹 Technology AI Models Machine Learning Data science Interpretability Natural Language
  1. Foundation models are really complex and hard to understand. They act like black boxes, which makes it tough to know how they make decisions.
  2. Unlike older machine learning models, these large models have much more advanced capabilities but also come with bigger interpretability challenges.
  3. New fields like mechanistic interpretability and behavioral probing are trying to help us figure out how these complex models work.

RAG, Hallucination & Structure: Research By ServiceNow

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 18 Apr 24
🕹 Technology AI Machine Learning Data science Natural Language Software Development
  1. ServiceNow is using a method called Retrieval-Augmented Generation (RAG) to help transform user requests in natural language into structured workflows. This aims to improve how easily users can create workflows without needing deep technical knowledge.
  2. By using RAG, they want to reduce 'hallucination', which is when AI generates wrong or irrelevant info, and make the AI more reliable. This is important for gaining user trust in AI systems.
  3. The study also suggests future improvements, like changing output formats for efficiency and streamlining processes so that users can see steps one at a time, making it easier to follow along.

LLM Disruption in Chatbot Development Frameworks

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Jul 24
🕹 Technology AI Chatbots Natural Language Programming Development
  1. Large Language Models (LLMs) make chatbots act more like humans, making it easier for developers to create smart bots.
  2. Using LLMs reduces the need for complex programming rules, allowing for quicker chatbot setup for different uses.
  3. Despite the benefits, there are still challenges, like keeping chatbots stable and predictable as they become more advanced.

Improve Conversational UIs Using Social Intelligence

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 09 Apr 24
🕹 Technology Artificial Intelligence Natural Language User Interface Machine Learning Data science
  1. Social intelligence is important for conversational AIs to feel more human-like. It helps them understand emotions and social cues better.
  2. A good conversational UI needs to consider cognitive, situational, and behavioral intelligence. This means the AI should know what you mean, the context of your words, and how to interact appropriately.
  3. Using more data and different types of information beyond just words can help improve how AIs communicate. This could include things like images and gestures to understand conversations better.

A Short History Of Chatbots

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

Can Conversation Designers Excel As Data Designers?

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

English is NOT a Programming Language 🤦

Sector 6 | The Newsletter of AIM • 79 implied HN points • 07 Feb 24
🕹 Technology Programming AI Software Natural Language Computing
  1. English has too many ambiguities to be a programming language. Programming needs precise rules, and English doesn't always follow them.
  2. Douglas Crockford, the creator of JSON, is worried about pushing English as a coding language. He believes that code must be perfect, which English is not.
  3. Using natural language through AI for programming might lead to confusion. Clarity and accuracy are crucial for writing successful code.

Large Impact: The Rise of Small Language Models

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

Data Science Weekly - Issue 495

Data Science Weekly Newsletter • 239 implied HN points • 19 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Natural Language Software Development
  1. Absence of evidence can often serve as strong evidence of absence, and this idea can be explored with Bayesian methods.
  2. Natural language processing is being used to analyze global supply chains, helping create networks from news articles.
  3. It's crucial to understand the unique challenges and opportunities in personalizing search results, as seen with Netflix's approach.

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

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

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

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

Please Stop Saying Long Context Windows Will Replace RAG

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

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

Language Model Quantization Explained

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

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

The Rise of the AI Dragon

Sector 6 | The Newsletter of AIM • 59 implied HN points • 04 Dec 23
🕹 Technology AI Open Source Machine Learning Data science Natural Language
  1. There are new AI models based on LLaMA, like DeepSeek, that are showing great performance. These models are pushing the boundaries of what AI can do.
  2. Chinese companies are making significant progress in open source AI models and many are now leading in popularity and performance.
  3. DeepSeek and other models are being developed with the goal of exploring artificial general intelligence, which aims to create more advanced AI systems.

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

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

Fine-Tuning OpenAI GPT-4o mini

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

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

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

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

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

LLMs Training SLMs

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

Microsoft’s New Love

Sector 6 | The Newsletter of AIM • 39 implied HN points • 17 Nov 23
🕹 Technology AI Development Machine Learning Natural Language Data science Software Engineering
  1. Large language models (LLMs) like ChatGPT are powerful but costly to run and customize. They require a lot of resources and can be tricky to adapt for specific tasks.
  2. Small language models (SLMs) are emerging as a better option because they are cheaper to train and can give more accurate results. They also don't need heavy hardware to operate.
  3. Many companies are starting to focus on developing small language models due to their efficiency and effectiveness, marking a shift in the industry.

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

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

Understanding LLM User Experience & Expectation

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

Data Prepare of Basic Retrieval Augmented Generation

The Beep • 19 implied HN points • 18 Jan 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Software Development Data processing
  1. Retrieval Augmented Generation (RAG) helps combine general language models with specific domain knowledge. It acts like a plugin that makes models smarter about particular topics.
  2. To prepare data for RAG, you need to load, split, and create vector stores from your documents. This process helps in organizing and retrieving relevant information efficiently.
  3. Using RAG can improve the accuracy of responses from language models. By providing context from relevant documents, you can reduce errors and make the information shared more reliable.

Could language models change language?

The Counterfactual • 119 implied HN points • 22 Jul 22
🕹 Technology Machine Learning Natural Language Communication Linguistics Innovation
  1. Language is shaped by how we use it, and machine learning models might influence our language by suggesting words or phrases. Over time, these suggestions could change the way we communicate.
  2. The widespread use of predictive text and language models could either slow down language change by promoting similar expressions, or lead to new and unexpected language innovations.
  3. We could see personalized language models that adapt to individual users, potentially changing how we write and understand language, and encouraging less need for clarity in communication.