The hottest Natural Language Processing Substack posts right now

And their main takeaways
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Top Technology Topics

ChatGPT in Shambles

Marcus on AI • 13161 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data Analysis Software Development
  1. ChatGPT still has major reliability issues, often providing incomplete or incorrect information, like missing U.S. states in tables.
  2. Despite being advanced, AI can still make basic mistakes, such as counting vowels incorrectly or misunderstanding simple tasks.
  3. Many claims about rapid progress in AI may be overstated, as even simple functions like creating tables can lead to errors.

Why reasoning models will generalize

Democratizing Automation • 1504 implied HN points • 28 Jan 25
🕹 Technology AI Machine Learning Data science Natural Language Processing Computing
  1. Reasoning models are designed to break down complex problems into smaller steps, helping them solve tasks more accurately, especially in coding and math. This approach makes it easier for the models to manage difficult questions.
  2. As reasoning models develop, they show promise in various areas beyond their initial focus, including creative tasks and safety-related situations. This flexibility allows them to perform better in a wider range of applications.
  3. Future reasoning models will likely not be perfect for every task but will improve over time. Users may pay more for models that deliver better performance, making them more valuable in many sectors.

Analyze research papers with Gemini 2.0

Gonzo ML • 126 implied HN points • 23 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Cloud Computing
  1. Gemini 2.0 models can analyze research papers quickly and accurately, supporting large amounts of text. This means they can handle complex documents like academic papers effectively.
  2. The DeepSeek-R1 model shows that strong reasoning abilities can be developed in AI without the need for extensive human guidance. This could change how future models are trained and developed.
  3. Distilling knowledge from larger models into smaller ones allows for efficient and accessible AI that can perform well on various tasks, which is useful for many applications.

LLMs and World Models, Part 1

AI: A Guide for Thinking Humans • 247 implied HN points • 13 Feb 25
🕹 Technology AI Machine Learning Neural Networks Natural Language Processing Computational Models
  1. In the past, AI systems often used shortcuts to solve problems rather than truly understanding concepts. This led to unreliable performance in different situations.
  2. Today’s large language models are debated to either have learned complex world models or just rely on memorizing and retrieving data from their training. There’s no clear agreement on how they think.
  3. A 'world model' helps systems understand and predict real-world behaviors. Different types of models exist, with some capable of capturing causal relationships, but it's unclear how well AI systems can do this.

Multilingual Embeddings, Safer LLMs, and Log-Linear Attention

ppdispatch • 2 implied HN points • 13 Jun 25
🕹 Technology AI Machine Learning Data science Natural Language Processing Computer Science
  1. There's a new multilingual text embedding benchmark called MMTEB that covers over 500 tasks in more than 250 languages. A smaller model surprisingly outperforms much larger ones.
  2. Saffron-1 is a new method designed to make large language models safer and more efficient, especially in resisting attacks.
  3. Harvard released a massive dataset of 242 billion tokens from public domain books, which can help in training language models more effectively.
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DeepSeek-R1: Open model with Reasoning

Gonzo ML • 126 implied HN points • 10 Feb 25
🕹 Technology AI Research Machine Learning Natural Language Processing Open Source Reinforcement Learning
  1. DeepSeek-R1 shows how AI models can think through problems by reasoning before giving answers. This means they can generate longer, more thoughtful responses rather than just quick answers.
  2. This model is a big step for open-source AI as it competes well with commercial versions. The community can improve it further, making powerful tools accessible for everyone.
  3. The training approach used is innovative, focusing on reinforcement learning to teach reasoning without needing a lot of examples. This could change how we train AI in the future.

LCM: Large Concept Model

Gonzo ML • 189 implied HN points • 04 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Computational Models
  1. The Large Concept Model (LCM) aims to improve how we understand and process language by focusing on concepts instead of just individual words. This means thinking at a higher level about what ideas and meanings are being conveyed.
  2. LCM uses a system called SONAR to convert sentences into a stable representation that can be processed and then translated back into different languages or forms without losing the original meaning. This creates flexibility in how we communicate.
  3. This approach can handle long documents more efficiently because it represents ideas as concepts, making processing easier. This could improve applications like summarization and translation, making them more effective.

What "language" is a language model a model of?

The Counterfactual • 99 implied HN points • 02 Aug 24
🕹 Technology AI Machine Learning Natural Language Processing Computational linguistics Data science
  1. Language models are trained on specific types of language, known as varieties. This includes different dialects, registers, and periods of language use.
  2. Using a representative training data set is crucial for language models. If the training data isn't diverse, the model can perform poorly for certain groups or languages.
  3. It's important for researchers to clearly specify which language and variety their models are based on. This helps everyone better understand what the model can do and where it might struggle.

WeKnow-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 16 Aug 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Information Retrieval
  1. WeKnow-RAG uses a smart approach to gather information that mixes simple facts from its knowledge base with data found on the web. This helps improve the accuracy of answers given to users.
  2. This system includes a self-check feature, which allows it to assess how confident it is in the information it provides. This helps to reduce mistakes and improve quality.
  3. Knowledge Graphs are important because they organize information in a clear way, allowing the system to find the right data quickly and effectively, no matter what type of question is asked.

Creating Synthetic Training Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Aug 24
🕹 Technology Artificial Intelligence Data science Machine Learning Natural Language Processing Software Development
  1. Creating synthetic data is hard because it's not just about making more data; it also needs to be diverse and varied. It's tough to make sure there are enough different examples.
  2. Using a seed corpus can limit how varied the synthetic data is. If the starting data isn't diverse, the generated data won't be either.
  3. A new approach called Persona Hub uses a billion different personas to create varied synthetic data. This helps in generating high-quality, interesting content across various situations.

ModernBERT, the BERT of 2024

Gonzo ML • 63 implied HN points • 19 Dec 24
🕹 Technology AI Machine Learning Natural Language Processing Computing Data science
  1. ModernBERT is a new version of BERT that improves processing speed and memory efficiency. It can handle longer contexts and makes BERT more practical for today's tasks.
  2. The architecture of ModernBERT has been updated with features that enhance performance, like better attention mechanisms and optimized computations. This means it works faster and can process more data at once.
  3. ModernBERT has shown impressive results in various natural language understanding tasks and can compete well against larger models, making it an exciting tool for developers and researchers.

Tokenization in large language models, explained

The Counterfactual • 239 implied HN points • 02 May 24
🕹 Technology AI Language Models Tokenization Machine Learning Natural Language Processing
  1. Tokens are the building blocks that language models use to understand and predict text. They can be whole words or parts of words, depending on how the model is set up.
  2. Subword tokenization helps models balance flexibility and understanding by breaking down words into smaller parts, so they can still work with unknown words.
  3. Understanding how tokenization works is key to improving the performance of language models, especially since different languages have different structures and complexity.

📝 Guest Post: Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring*

TheSequence • 126 implied HN points • 31 Jan 25
🕹 Technology Natural Language Processing Data science Machine Learning Artificial Intelligence Software Development
  1. Augmented SBERT (AugSBERT) improves sentence scoring tasks by using data augmentation to create more sentence pairs. This means it can perform better even when there's not much training data available.
  2. Traditional methods like cross-encoders and bi-encoders have limitations, like being slow or needing a lot of data. AugSBERT addresses these issues, making it more efficient for large-scale tasks.
  3. The approach combines the strengths of different models to enhance performance, especially in specific domains. It shows significant improvements over existing models, making it a useful tool for various natural language processing applications.

Large language models, explained with a minimum of math and jargon

The Counterfactual • 599 implied HN points • 28 Jul 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing Software Development
  1. Large language models, like ChatGPT, work by predicting the next word based on patterns they learn from tons of text. They don’t just use letters like we do; they convert words into numbers to understand their meanings better.
  2. These models handle the many meanings of words by changing their representation based on context. This means that the same word could have different meanings depending on how it's used in a sentence.
  3. The training of these models does not require labeled data. Instead, they learn by guessing the next word in a sentence and adjusting their processes based on whether they are right or wrong, which helps them improve over time.

The Sequence Knowledge #473: Not All RAGs are Created Equal

TheSequence • 98 implied HN points • 21 Jan 25
🕹 Technology Machine Learning Artificial Intelligence Data science Information Retrieval Natural Language Processing
  1. RAG stands for Retrieval Augmented Generation. It's a way for machines to pull in outside information, helping them give better and more accurate answers.
  2. There are many kinds of RAG, like Standard RAG and Fusion RAG. Each type helps machines deal with different problems and has its special strengths.
  3. Understanding these RAG types is important for anyone working in AI. It helps them choose the right approach for different challenges.

The Sequence Knowledge #468: A New Series About RAG

TheSequence • 84 implied HN points • 13 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing Research
  1. Retrieval Augmented Generation, or RAG, helps AI models use outside information to improve their answers. This makes the responses more accurate and relevant.
  2. RAG works in two steps: first, it finds useful information, and then it uses that information to create better responses. This method is great for applications that need quick and correct answers.
  3. A key paper introduced RAG and showed that combining different types of memory can lead to better results in language tasks, like answering questions or generating text.

Gsnowflake

benn.substack • 792 implied HN points • 07 Jul 23
🕹 Technology Data AI Cloud Computing Natural Language Processing
  1. Google is technically a database but differs from traditional databases in its structure and content.
  2. Snowflake is introducing features like Document AI that hint at a shift towards focusing on information retrieval rather than just data analysis.
  3. The market for an information database could potentially be larger and more accessible than traditional data warehouses, offering simpler access to basic facts and connections.

The Large Language Model Landscape — Version 5

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 79 implied HN points • 25 Apr 24
🕹 Technology AI Machine Learning Software Development Natural Language Processing Data science
  1. Large Language Models (LLMs) are evolving with more functionality, combining various tasks into fewer models. This helps in making them more efficient for users.
  2. There are different zones in the LLM landscape, each focusing on specific uses, tools, and applications, ranging from available models to user interfaces.
  3. Tech advancements like prompt engineering and data-centric tools are making it easier to harness the power of LLMs, opening up new opportunities for businesses.

Imitation Models and the Open-Source LLM Revolution

Deep (Learning) Focus • 294 implied HN points • 19 Jun 23
🕹 Technology Deep Learning Natural Language Processing Open Source
  1. Creating imitation models of powerful LLMs is cost-effective and easy but may not perform as well as proprietary models in broader evaluations.
  2. Model imitation involves fine-tuning a smaller LLM using data from a more powerful model, allowing for behavior replication.
  3. Open-source LLMs, while exciting, may not close the gap between paid and open-source models, highlighting the need for rigorous evaluation and continued development of more powerful base models.

Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based Distillation

TheSequence • 77 implied HN points • 17 Dec 24
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Natural Language Processing
  1. Attention-based distillation (ABD) is a method that helps smaller models learn from larger models by mimicking their attention patterns. This can make the smaller models perform better with fewer resources.
  2. Unlike traditional methods that just look at output predictions, ABD focuses on the reasoning process of the larger model. This leads to a deeper understanding and better results for the smaller model.
  3. Using ABD can produce student models that perform well even when they have less complexity. This is useful for applications where efficiency is key.

The Importance Of Granular Data Design For Fine-Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 02 May 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Granular data design helps improve the behavior and abilities of language models. This means making training data more specific so the models can reason better.
  2. New methods like Partial Answer Masking allow models to learn self-correction. This helps them improve their responses without needing perfect answers in the training data.
  3. Training models with a focus on long context helps them retrieve information more effectively. This approach tackles issues where models can lose important information in lengthy input.

Math TTS, VideoRAG, and Self-Adaptive LLMs

HackerPulse Dispatch • 5 implied HN points • 17 Jan 25
🕹 Technology AI Machine Learning Speech Recognition Natural Language Processing Video Processing
  1. MathReader turns math documents into speech, making it easier for people to access and understand math content.
  2. VideoRAG helps improve language generation by pulling in relevant video content, which can provide more context than text alone.
  3. ELIZA, the first chatbot ever created, has been restored, so people can see how early AI worked and explore its historical significance.

Notes: LLMs don't know what they are talking about

aspiring.dev • 2 HN points • 15 Sep 24
🕹 Technology AI Machine Learning Content Generation Natural Language Processing
  1. LLMs can be tricked into creating harmful content even when they are programmed not to. They don't really understand the context of what they generate.
  2. The way LLMs handle safety is based on prompts, not the content they produce. If the prompt can be manipulated, the output can be too.
  3. There are suggestions for improving LLM safety, like analyzing outputs during and after generation, rather than only checking the initial request.

Better AI Creative Writing?

Jakob Nielsen on UX • 23 implied HN points • 27 Nov 24
🕹 Technology AI Creative Writing Machine Learning Natural Language Processing User Experience
  1. The latest version of ChatGPT showed some improvement in creative writing over the past year, especially in children's stories. It produced longer stories with more engaging content.
  2. When it comes to writing poetry, the changes were minor. The recent poems didn't stand out much compared to last year's efforts.
  3. Overall, while there's some progress in AI writing skills, it's still quite limited. Bigger advancements are expected in the next generation of AI models.

Speculative RAG By Google Research

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 12 Jul 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computing
  1. Retrieval Augmented Generation (RAG) is a way to improve answers by using a mix of information from language models and external sources. By doing this, it gives more accurate and timely responses.
  2. The new Speculative RAG method uses a smaller model to quickly create drafts from different pieces of information, letting a larger model check those drafts. This makes the whole process faster and more effective.
  3. Using smaller, specialized language models for drafting helps save on costs and reduces wait times. It can also improve the accuracy of answers without needing extensive training.

Maximizing the Potential of Large Language Models

Gradient Flow • 359 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Data Management Data science Natural Language Processing
  1. Language models need a three-pronged strategy of tuning, prompting, and rewarding to unlock their full potential.
  2. Fine-tuning pre-trained models is a common practice to tailor models for specific tasks and domains.
  3. Teams require simple and versatile tools to create custom models efficiently and effectively.

The Tech Buffet #17: 9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems

The Tech Buffet • 139 implied HN points • 02 Jan 24
🕹 Technology Artificial Intelligence Natural Language Processing Data Management Software Development Cloud Computing
  1. Make sure the data you use for RAG systems is clean and accurate. If you start with bad data, you'll get bad results.
  2. Finding the right size for document chunks is important. Too small or too large can affect the quality of the information retrieved.
  3. Adding metadata to your documents can help organize search results and make them more relevant to what users are looking for.

Complete Summary of Absolute, Relative and Rotary Position Embeddings!

Aziz et al. Paper Summaries • 79 implied HN points • 31 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Computing Data science
  1. Transformers can't understand the order of words, so position embeddings are used to give them that context.
  2. Absolute embeddings assign unique values to each word's position, but they struggle with new positions beyond what they trained on.
  3. Relative embeddings focus on the distance between words, which makes the model aware of their relationships, but they can slow down training and searching.

ChatGPT Explained: A Normie's Guide To How It Works

jonstokes.com • 587 implied HN points • 01 Mar 23
🕹 Technology Machine Learning AI Language Models Model Training Natural Language Processing
  1. Understand the basics of generative AI: a generative model produces a structured output from a structured input.
  2. Complex relationships between symbols require more computational power to relate them effectively.
  3. Language models like ChatGPT don't have personal experiences or knowledge; they use a token window to respond based on the conversation context.

E2- Basics of Large Language Models for Product Managers 🤖

The Product Channel By Sid Saladi • 16 implied HN points • 17 Nov 24
🕹 Technology AI Product Management Innovation Natural Language Processing Data science
  1. Large language models (LLMs) are special AI systems that understand and generate human language. They can do things like summarize texts, translate languages, and even write codes.
  2. LLMs are changing many industries by powering chatbots, helping create content, and giving personalized product recommendations. This makes services smarter and more helpful.
  3. Building custom LLMs requires a lot of money and data. Companies must invest millions and gather vast amounts of information to develop effective models.

Tree Of Thoughts Prompting (ToT)

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

The Tech Buffet #18: Advanced Retrieval Techniques for RAG

The Tech Buffet • 79 implied HN points • 08 Jan 24
🕹 Technology AI Machine Learning Information Retrieval Natural Language Processing Data processing
  1. Query expansion helps make searches better by changing the way a question is asked. This can include generating example answers or related questions to find more useful information.
  2. Cross-encoder re-ranking improves the results by scoring how relevant documents are to a search query. This way, only the most helpful documents get selected for easy viewing.
  3. Embedding adaptors are a simple tool to adjust document scoring, making it easier to align the search results with what users need. Using these methods together can significantly enhance the effectiveness of document retrieval.

Controllable Agents For RAG With Human In The Loop Chat

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

Understanding the Different Types of Transformers in AI [Math Mondays]

Technology Made Simple • 99 implied HN points • 11 Jul 23
🕹 Technology AI Deep Learning Neural Networks Machine Learning Natural Language Processing
  1. There are three main types of transformers in AI: Sequence-to-Sequence Models excel at language translation tasks, Autoregressive Models are powerful for text generation but may lack deeper understanding, and Autoencoding Models focus on language understanding and classification by capturing meaningful representations of input data.
  2. Transformers with different training methodologies influence their performance and applicability, so understanding these distinctions is crucial for selecting the most suitable model for specific use cases.
  3. Deep learning with transformer models offers a diverse range of capabilities, each catering to unique needs: mapping sequences between languages, generating text, or focusing on language understanding and classification.

The Conversational AI Technology Landscape: Version 5.0

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

The Case For Small Language Models

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

How could we know if Large Language Models understand language?

The Counterfactual • 219 implied HN points • 18 Oct 22
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Computing Data science
  1. There's a big debate about whether large language models truly understand language or if they're just mimicking patterns from the data they were trained on. Some people think they can repeat words without really grasping their meaning.
  2. Two main views exist: One says LLMs can't understand language because they lack deeper meaning and intent, while the other argues that if they behave like they understand, then they might actually understand.
  3. As LLMs become more advanced, we need to create better ways to test their understanding. This will help us figure out what it really means for a machine to 'understand' language.

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

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