The hottest Natural Language Processing Substack posts right now

And their main takeaways
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Top Technology Topics

How to Fine-Tune Your Own Mistral-7B

The Beep • 39 implied HN points • 14 Jan 24
🕹 Technology Machine Learning Natural Language Processing AI Models Data science Programming
  1. You can fine-tune the Mistral-7B model using the Alpaca dataset, which helps the model understand and follow instructions better.
  2. The tutorial shows you how to set up your environment with Google Colab and install necessary libraries for training and tracking the model's performance.
  3. Once you prepare your data and configure the model, training it involves monitoring progress and adjusting settings to get the best results.

In cautious defense of LLM-ology

The Counterfactual • 119 implied HN points • 02 Mar 23
🕹 Technology AI Development Machine Learning Natural Language Processing Cognitive Science Human-computer interaction
  1. Studying large language models (LLMs) can help us understand how they work and their limitations. It's important to know what goes on inside these 'black boxes' to use them effectively.
  2. Even though LLMs are man-made tools, they can reflect complex behaviors that are worth studying. Understanding these systems might reveal insights about language and cognition.
  3. Research on LLMs, known as LLM-ology, can provide valuable information about human mind processes. It helps us explore questions about language comprehension and cognitive abilities.

Natural Language Is an Unnatural Interface

Public Experiments • 154 HN points • 27 Jun 23
🕹 Technology AI Interfaces Natural Language Processing AI Applications Open-source models
  1. Natural language interfaces for AI are challenging due to the vast degree of freedom in text input.
  2. Prompt engineering is crucial for effectively utilizing large language models to ensure correct and meaningful responses.
  3. For most users, interacting with AI systems through buttons and defined interfaces can lead to more efficient and seamless experiences compared to using natural language prompts.

Prompt-RAG

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

Papers I've read this week: vision language models

Artificial Fintelligence • 8 implied HN points • 28 Oct 24
🕹 Technology AI Models Computer Vision Machine Learning Natural Language Processing Research Papers
  1. Vision language models (VLMs) are simplifying how we extract text from images. Unlike older software, modern VLMs make this process much easier and faster.
  2. There are several ways to combine visual and text data in VLMs. Most recent models prefer a straightforward approach of merging image features with text instead of using complex methods.
  3. Training a VLM involves using a good vision encoder and a pretrained language model. This combination seems to work well without any major drawbacks.
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Catastrophic Forgetting In LLMs

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

Five Stages Of LLM Implementation [Updated]

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

The Tech Buffet #9: Let's talk about LLM Hallucinations

The Tech Buffet • 39 implied HN points • 24 Oct 23
🕹 Technology AI Machine Learning Data science Natural Language Processing Software Development
  1. LLMs, or Large Language Models, often produce incorrect or misleading information, known as hallucinations. This happens because they generate text based on probabilities, not actual understanding.
  2. To measure how factually accurate LLM responses are, a tool called FActScore can break down answers into simple facts and check if these facts are true. This helps in gauging the accuracy of the information given by LLMs.
  3. To reduce hallucinations, it's important to implement strategies such as allowing users to edit AI-generated content, providing citations, and encouraging detailed prompts. These methods can help improve the trustworthiness and reliability of the information LLMs produce.

Conversations with Claude

The Day After Tomorrow • 19 implied HN points • 10 Mar 24
🕹 Technology AI Models Machine Learning Ethics Human-AI Interaction Natural Language Processing
  1. Claude 3 has shown impressive conversational skills, feeling more human-like compared to other AI models like GPT-4. This makes interactions feel more natural.
  2. The AI has a complex understanding of ethical decision-making, stating that it prioritizes human well-being and aims to provide helpful information while avoiding harm.
  3. In moral dilemmas, Claude 3's rankings on the value of life are intriguing. It sometimes values non-human entities, like whales, over humans, showcasing a unique perspective on morality.

GPT-4 captures judgments about semantic relatedness quite well

The Counterfactual • 59 implied HN points • 18 May 23
🕹 Technology Artificial Intelligence Natural Language Processing Cognitive Science Data Analysis
  1. GPT-4 is really good at understanding word similarities. In tests, it matched human opinions better than many expected.
  2. Sometimes GPT-4 thinks that certain words are more similar than people do. It tends to view pairs of words like 'wife' and 'husband' as more alike than humans generally agree on.
  3. Using GPT-4 for semantic questions could save time and money in research, but it's still important to include human input to avoid biases.

On the "Death of the Software Engineer by GenAI"

Laszlo’s Newsletter • 64 implied HN points • 13 Nov 23
🕹 Technology AI Software Engineering Generative AI Programming Natural Language Processing
  1. Software engineering has drastically improved over the years with advancements in tools and techniques like high-level abstractions and unit testing.
  2. Natural language is not suited for specifying programming instructions due to its imprecise nature, unlike the detailed specs required for coding.
  3. Generative models like ChatGPT can assist in programming tasks and improve efficiency, but they won't replace the need for human software engineers.

Must Learn AI Security Part 20: Text-based Attacks Against AI

Rod’s Blog • 39 implied HN points • 03 Oct 23
🕹 Technology AI Security Natural Language Processing Adversarial Attacks Cybersecurity
  1. Text-based attacks against AI target natural language processing systems like chatbots and virtual assistants by manipulating text to exploit vulnerabilities.
  2. Various types of text-based attacks include misclassification, adversarial examples, evasion attacks, poisoning attacks, and hidden text attacks which deceive AI systems with carefully crafted text.
  3. Text-based attacks against AI can lead to misinformation, security breaches, bias and discrimination, legal violations, and loss of trust, highlighting why organizations need to implement measures to detect and prevent such attacks.

Overview of Existing Research of LLMs for Healthcare

AI for Healthcare • 19 implied HN points • 07 Feb 24
🔬 Science Healthcare Natural Language Processing
  1. Large Language Models (LLMs) in healthcare have the potential to revolutionize tasks like document summarization and text classification.
  2. LLM research in the medical domain involves using LLMs directly on medical tasks, fine-tuning existing LLMs for medical data, and training medical LLMs from scratch.
  3. There is a need to focus on training LLMs on real-world hospital data for more accurate and practical applications in healthcare.

Popular LLM Datasets

The Beep • 19 implied HN points • 21 Jan 24
🕹 Technology Machine Learning Data science Artificial Intelligence Natural Language Processing Software Development
  1. Datasets are crucial for training machine learning models, including language models. They help the model learn patterns and make predictions.
  2. Popular sources for datasets include Project Gutenberg and Common Crawl, which provide large amounts of text data for training language models.
  3. Instruction tuning datasets are used to adapt pre-trained models for specific tasks. These help the model perform better in given situations or instructions.

How Should Large Language Models Be Evaluated?

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

Updated: Emerging RAG & Prompt Engineering Architectures for LLMs

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

A Book to Make You Less Afraid of AI: Melanie Mitchell's "Artificial Intelligence"

Mike Talks AI • 19 implied HN points • 14 Jul 23
🔬 Science AI Neural Networks Natural Language Processing Algorithms
  1. The book 'Artificial Intelligence' by Melanie Mitchell eases fears about AI and provides education.
  2. It covers the history of AI, details on algorithms, and a discussion on human intelligence.
  3. The book explains how deep neural networks and natural language processing work in an understandable way.

Emu Video Edit , General game-playing AI agent, fully autonomous AI software engineer, DeepSeek-VL, Robotics Foundation Model, and more

AI Brews • 17 implied HN points • 15 Mar 24
🕹 Technology AI Generative models Robotics Natural Language Processing
  1. DeepSeek-VL is a new vision-language model for real-world applications with competitive performance.
  2. Cognition Labs introduces Devin, the first fully autonomous AI software engineer, capable of learning, building, and deploying apps.
  3. The European Parliament approved the Artificial Intelligence Act, which bans certain AI applications including biometric categorization and emotion recognition in specific contexts.

Newsletter #13: StructGPT

Decoding Coding • 19 implied HN points • 25 May 23
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. StructGPT helps large language models (LLMs) work better with structured data like graphs and databases. It converts this complex data into a simpler format that LLMs can understand.
  2. There are three key tasks that StructGPT can do: answer questions based on knowledge graphs, process data tables, and perform text-to-SQL queries. Each task has its own specific steps.
  3. The method focuses on linearizing raw data so that LLMs can process it more effectively. This allows LLMs to handle a wider variety of tasks more efficiently.

Does GPT-3 read between the lines?

The Counterfactual • 39 implied HN points • 19 Sep 22
🕹 Technology AI Language Models Natural Language Processing Computational linguistics Machine Learning
  1. GPT-3 understands 'some' to mean 2 out of 3 letters, but it doesn't change this meaning based on how much information the speaker knows. Humans, however, adjust their understanding based on the context.
  2. When asked if the speaker knows how many letters have checks, GPT-3 gives the right answer if asked before the speaker uses specific words, like 'some' or 'all'. But afterwards, it relies on those words too much.
  3. GPT-3's way of interpreting language is different from how humans do it. It seems to have a fixed meaning for words without considering the situation, unlike humans who use context to understand better.

"Mechanistic interpretability" for LLMs, explained

The Counterfactual • 1 HN point • 08 Jul 24
🕹 Technology AI Research Machine Learning Natural Language Processing Computer Science Data science
  1. Mechanistic interpretability helps us understand how large language models (LLMs) like ChatGPT work, breaking down their 'black box' nature. This understanding is important because we need to predict and control their behavior.
  2. Different research methods, like classifier probes and activation patching, are used to explore how components in LLMs contribute to their predictions. These techniques help researchers pinpoint which parts of the model are responsible for specific tasks.
  3. There's a growing interest in this field, as researchers believe that knowing more about LLMs can lead to safer and more effective AI systems. Understanding how they work can help prevent issues like bias and deception.

Week 74 - E2- Basics of Large Language Models for Product Managers 🤖

The Product Channel By Sid Saladi • 13 implied HN points • 14 Jan 24
🕹 Technology AI Product Management Natural Language Processing Innovation User Experience
  1. Large language models (LLMs) are transforming industries with diverse applications like automated article generation, conversational product recommendations, intelligent chatbots, and code generation.
  2. LLMs play a crucial role in product innovation by assisting in rapid ideation, prototyping, concept validation, and continuous enhancement of offerings.
  3. Understanding the costs and data requirements to develop LLMs is essential, as it involves significant investment in computational resources, data training, and cloud infrastructure.

Data Science Weekly - Issue 461

Data Science Weekly Newsletter • 19 implied HN points • 22 Sep 22
🕹 Technology Data science Machine Learning Natural Language Processing Artificial Intelligence Software Development
  1. Working in Natural Language Processing (NLP) involves keeping up with evolving models and figuring out how to effectively use data. It's still challenging for many to find practical applications for NLP.
  2. Generative AI has the potential to make workers significantly more efficient and creative. This could result in substantial economic value across various industries.
  3. Building trust in machine learning is crucial but challenging. It's important to address concerns about model reliability to maximize its business value.

Data Science Weekly - Issue 447

Data Science Weekly Newsletter • 19 implied HN points • 16 Jun 22
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Natural Language Processing
  1. Natural language processing is getting better, but it's important to remember that it's just imitating consciousness, not actually having it.
  2. Scaling AI models may improve performance, but there are limits due to the quality of the data they learn from.
  3. Emerging techniques like optical neural networks are being developed to speed up image classification significantly.

Data Science Weekly - Issue 432

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 22
🕹 Technology AI Machine Learning Data science Software Engineering Natural Language Processing
  1. AI art has evolved quickly, becoming more relatable and controllable thanks to advancements in technology. Many people, even experts, are surprised by how realistic and detailed AI-generated images can now be.
  2. Conversational agents, like chatbots, are becoming more common and can serve different purposes, from casual chats to helping users complete specific tasks. However, understanding their impact on society is important as they become more integrated into daily life.
  3. The CX-ToM framework improves explainable AI by creating a dialogue between machines and humans for better understanding. This approach focuses on the intentions of both the user and the machine, making AI decisions clearer.

The game of semantic promises (Or, why you are not using Siri)

Salami dev blog • 1 HN point • 09 Apr 24
🕹 Technology Natural Language Processing AI User Experience Automation
  1. Implicit promises in language communication can lead to awkward or failed interactions.
  2. Natural Language Interfaces like Siri may not truly understand the complexities of language, leading to communication challenges.
  3. The sub-languages created by technology interfaces can be confusing and ever-changing, making users hesitant to rely on them for important tasks.

The Sorcerer’s Apprentice: Applied AI for Data

Perspectives • 3 implied HN points • 09 Feb 24
🕹 Technology AI Data Analytics Natural Language Processing Automation Machine Learning
  1. Illustrates the importance of utilizing AI in data analytics wisely to avoid potential risks and maximize benefits
  2. Provides practical tips on how to apply AI in data work, such as using tools for natural language processing, coding assistance, and documentation
  3. Highlights the gap between current AI capabilities and the ideal automation of analytics, emphasizing the role of asking the right questions in data work

Amazon BASE TTS Makers claim "Emergent Abilities"

Machine Economy Press • 2 implied HN points • 22 Feb 24
🕹 Technology AI Data science Text-to-Speech Generative AI Natural Language Processing
  1. Amazon has developed a new, massive text-to-speech model called BASE TTS with emergent abilities, enhancing its natural speech capabilities for AI assistants like Alexa.
  2. The 980 million parameter BASE TTS model is significant for audio and NLP advancements, as it's the largest text-to-speech model created so far.
  3. Text-to-speech and NLP innovations are paving the way for more human-like interactions with voice assistants, marking a shift towards ambient computing.

Data Science Weekly - Issue 328

Data Science Weekly Newsletter • 19 implied HN points • 05 Mar 20
🕹 Technology Data science Machine Learning Artificial Intelligence Neuroscience Natural Language Processing
  1. The brain is not like a computer. Many scientists believe we might be misunderstanding how our brains work by using this comparison.
  2. BERT models are widely used in language processing, but we still need to learn more about how they really function.
  3. Understanding machine learning doesn't have to be complicated. There are resources that explain it in simple terms with practical examples for everyone.

Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 19 implied HN points • 18 Jul 19
🕹 Technology Data science Machine Learning Artificial Intelligence Natural Language Processing Deep Learning
  1. Netflix is moving away from traditional collaborative filtering methods to improve its recommendation system.
  2. Using AI and natural language processing (NLP) can help companies better understand and meet customer requests.
  3. It's important to audit AI systems to check for bias, especially when making significant decisions like loans or legal verdicts.

Using Large Language Models Effectively

Unsupervised Learning • 3 HN points • 27 Feb 23
🕹 Technology AI Machine Learning Natural Language Processing Artificial Intelligence Data science
  1. Large language models like ChatGPT have sparked interest across companies for various use cases.
  2. Companies can start implementing LLM capabilities with small, nimble teams for rapid experimentation.
  3. Key lessons include prioritizing user experience, starting with lower stakes tasks, and ensuring trust and safety in LLM features.

Technical Dive Into AutoGPT

Sudo Apps • 2 HN points • 22 Apr 23
🕹 Technology AI Machine Learning Open Source Autonomous Agents Natural Language Processing
  1. Auto-GPT uses various techniques to make GPT autonomous in completing tasks with executable commands.
  2. Auto-GPT addresses GPT's lack of explicit memory by using external memory modules like embeddings and vector storage.
  3. Interpreting responses with fixed JSON format and executing commands allows Auto-GPT to interact with the real world and complete tasks.

Data Science Weekly - Issue 193

Data Science Weekly Newsletter • 19 implied HN points • 03 Aug 17
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Salesforce is working on making artificial intelligence easier to use by automating how machine learning models are created.
  2. There's an important debate in social science about what counts as strong evidence in research, especially regarding the use of p-values.
  3. AI is being used in fun ways, like teaching machines to develop language skills and even create their own dance moves by watching games.

Structure Response from LLMs

mayt writes • 1 HN point • 02 Aug 23
🕹 Technology AI & Machine Learning Sentiment Analysis APIs Natural Language Processing
  1. Large Language Models (LLMs) can process unstructured text data to find information, summarize, and answer basic questions.
  2. Developers face challenges in handling unstructured data generated by LLMs and desire structured outputs for easier processing.
  3. By using novel features like function calling in LLMs, structured data can be generated for specific tasks like sentiment analysis, making data handling more efficient.

Data Science Weekly - Issue 119

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 16
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Visualization
  1. Data science can reveal hidden insights, like analyzing the language used in presidential debates to understand candidates better.
  2. AI is becoming more creative, as seen when Google's AI sold art for charity, showing its ability to create valuable pieces.
  3. Social media data can tell interesting stories, like an interactive map of Instagram posts in Hong Kong which shows the city's life based on user activity.

Data Science Weekly - Issue 17

Data Science Weekly Newsletter • 19 implied HN points • 20 Mar 14
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Analytics
  1. Data science is being used to uncover important insights in political analysis, such as studying the speeches of leaders like President Obama.
  2. Deep learning is a rapidly growing field that could reshape the world of analytics and has attracted attention from major tech companies.
  3. There are ongoing debates about the best programming languages for data analysis, with R and Python being the top contenders among data scientists.