The hottest Natural Language Processing Substack posts right now

And their main takeaways
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Top Technology Topics

Beyond the Hype: Understanding the Science Behind AI and LLMs

The Novice • 0 implied HN points • 12 Nov 23
🕹 Technology AI Machine Learning Neural Networks Natural Language Processing Deep Learning
  1. Word2Vec created word associations in 3D space but didn't understand word meanings.
  2. Generative Pretrained Transformers (GPTs) improved upon Word2Vec by understanding word context and relationships.
  3. Chat GPT appears smart by storing and retrieving vast amounts of data quickly, but it's not truly intelligent.

The State of LLM Based Application Development

Iceberg • 0 implied HN points • 19 Oct 23
🕹 Technology Interface Design Data handling Natural Language Processing Application development
  1. LLMs are gaining popularity in the tech world, especially through chat interfaces like Chat GPT models.
  2. Developers face challenges when transitioning human-to-machine interfaces to machine-to-machine interactions with LLMs.
  3. Tools like adjusting temperature parameters and utilizing frameworks can help overcome issues like hallucinations, context size limitations, and arbitrary output in LLM applications.

Zoo of RAGs

Shchegrikovich’s Newsletter • 0 implied HN points • 11 Feb 24
🕹 Technology AI Machine Learning Information Retrieval Natural Language Processing
  1. Retrieval Augmented Generation (RAG) improves LLM-based apps by providing accurate, up-to-date information through external documents and embeddings.
  2. RAPTOR enhances RAG by creating clusters from document chunks and generating text summaries, ultimately outperforming current methods.
  3. HiQA introduces a new RAG perspective with its Hierarchical Contextual Augmentation approach, utilizing Markdown formatting, metadata enrichment, and Multi-Route Retrieval for document grounding.
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The Genesis of Smart News Scraper

Shubhi’s Substack • 0 implied HN points • 17 Mar 18
🕹 Technology Coding Machine Learning Natural Language Processing Web Development
  1. Building a news scraper involved challenges like writing crawlers, applying machine learning concepts, and using Natural Language Processing.
  2. Collaborating with others and seeking help when needed led to valuable insights and the discovery of useful resources and libraries like NLTK and Naive Bayes Classifier.
  3. The project's outcome included the development of a Smart News Scraper, with room for improvement in accuracy, filters, multithreading, and expansion to cover news relevant to more colleges.

Asking the right questions on Apple

Joshua Gans' Newsletter • 0 implied HN points • 22 May 16
🕹 Technology Artificial Intelligence Big Data Natural Language Processing Tech investment
  1. Apple's potential risk with AI: The article discusses how Google's advancements in AI could pose a threat to Apple, especially in big-data services and AI where Apple lags behind.
  2. The importance of in-house AI development: The importance of Apple investing in in-house AI talent and assets is highlighted to remain competitive, rather than relying on partnerships or acquisitions.
  3. Need for innovation and adaptation: The article emphasizes the need for Apple to adapt to potential industry shifts in AI interfaces, stay aware of dominant design trends, and align their capabilities accordingly.

Reports of our death are an exaggeration Part 2

The Jolly Contrarian • 0 implied HN points • 24 Nov 23
🕹 Technology AI Machine Learning Artificial Intelligence Natural Language Processing Data science
  1. Machines are best utilized for tasks where human capabilities fall short, not to replace human intelligence entirely.
  2. Creating a division of labor between human intelligence and machines can optimize productivity by focusing each on their strengths.
  3. Artificial intelligence should not be used to simplify or homogenize cultural diversity, but rather to enhance human creativity and uniqueness.

Gradient Flow #43: Graph Databases; Language Understanding; Program Synthesis

Gradient Flow • 0 implied HN points • 09 Sep 21
🕹 Technology Data Management Machine Learning Natural Language Processing Deep Learning Forecasting
  1. Graph databases and graph analytics are growing in interest, with use cases and applications expanding.
  2. The NLP Summit offers insights from leading organizations and researchers in the field of Natural Language Processing.
  3. Tools like Darts for time series forecasting and River for online machine learning are open-source libraries enabling easier adoption of advanced machine learning techniques.

What If A.I. Just Disappoints Us?

The Digital Anthropologist • 0 implied HN points • 08 Mar 24
🕹 Technology Artificial Intelligence Generative AI Machine Learning Natural Language Processing
  1. AI may not live up to the grand promises or catastrophic fears set for it, but change is inevitable as with past technologies.
  2. There's a real possibility that AI might just fizzle out due to factors like limited electricity, quantum computing breakthroughs, or water scarcity.
  3. Generative AI tools could reach a limit in their advancements, settling to quietly assist in mundane or important tasks rather than revolutionize entire industries.

Modifying readability, pt. 2: the human study

The Counterfactual • 0 implied HN points • 01 Apr 24
🕹 Technology Natural Language Processing Artificial Intelligence Human Interaction
  1. The study tested whether human readers find text easier or harder to read when modified by a large language model. The results showed that people did indeed rate the 'easier' texts as more readable than the 'harder' ones.
  2. While different readability metrics correlated with human ratings, they were often more aligned with each other than with actual human judgment. This suggests that while these tools can help gauge readability, they might not capture all aspects of what makes a text readable.
  3. The research highlights that 'readability' is complex and subjective. Future studies should explore how different audiences might interpret readability, and consider other factors like comprehension and enjoyment when assessing text.

LLM-Driven Synthetic Data Generation, Curation & Evaluation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Aug 24
🕹 Technology AI Data science Machine Learning Natural Language Processing Synthetic Data
  1. Human oversight is key when generating synthetic data. It helps catch mistakes and ensure the data is useful for training models.
  2. Data quality and variety matter a lot in training language models. The better the data design, the better the model learns and performs.
  3. A solid structure for data creation can improve the efficiency and accuracy of generating synthetic data. This makes it more relevant to real-world applications.

Fine-Tuning or RAG?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Feb 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Tech development
  1. Choosing between fine-tuning and RAG depends on costs, available data, and model performance. It's important to weigh the benefits against the money and effort needed.
  2. RAG is often preferred because it provides context for questions and is easier to maintain. Fine-tuning can sometimes hurt the model due to forgetting past information.
  3. While both approaches have strengths, RAG often outperforms fine-tuning by including relevant knowledge and context. Experimenting with different models can lead to better results.

Beyond Chain-of-Thought LLM Reasoning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Feb 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Logic
  1. Indirect reasoning helps solve problems where direct reasoning fails. It uses logic to make connections that LLMs might struggle with.
  2. This approach significantly improves accuracy in tasks like factual reasoning and mathematical proofs. It shows better performance compared to methods that rely only on direct reasoning.
  3. The study suggests using simple prompts to guide LLMs in applying indirect reasoning, making it easier and more effective without needing complex frameworks.

Chain-of-Symbol Prompting (CoS) For Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Jan 24
🕹 Technology Artificial Intelligence Natural Language Processing Machine Learning Prompt engineering
  1. LLMs struggle with understanding complex spatial tasks using just natural language. This research focuses on improving their ability to navigate virtual environments.
  2. The new Chain-of-Symbol Prompting (CoS) method helps LLMs represent spatial relationships more effectively. It leads to much better performance in planning tasks compared to traditional methods.
  3. Using symbols instead of natural language makes it easier for LLMs to learn and reduces the number of tokens needed in prompts. This results in clearer and more concise representations.

Large Language Model Hallucination Mitigation Techniques

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 10 Jan 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computational linguistics
  1. There are many techniques to prevent hallucinations in large language models. They can be grouped into two types: methods that adjust the model itself and those that change how you ask it questions.
  2. Some effective techniques include using retrieval-augmented generation and prompting the model carefully. This means providing clear context and expected outcomes before asking for information.
  3. To best reduce hallucinations, combining different strategies is key. No single method works perfectly, so using a mix of approaches helps improve the model's accuracy and reliability.

Random Chain-Of-Thought For LLMs & Distilling Self-Evaluation Capability

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Software Development
  1. Complexity in processing data for large language models (LLMs) is growing. Breaking tasks into smaller parts is becoming a standard practice.
  2. LLMs are now handling tasks that used to require human supervision, such as generating explanations or synthetic data.
  3. Providing detailed context during inference is crucial to avoid mistakes and ensure better responses from LLMs.

Teaching LLMs To Say, “I don’t know”

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 04 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Processing Chatbots
  1. Large Language Models (LLMs) often give answers even when they don't know, which can lead to incorrect information. It's important for them to learn to say 'I don't know' instead.
  2. A new method called R-Tuning can help LLMs understand their limits by recognizing when they don't have enough information. This approach improves their ability to refuse answering unknowable questions.
  3. By identifying gaps in their knowledge, LLMs can be trained better to avoid giving false answers, making them more reliable and accurate in conversation.

LLM Performance Over Time & Task Contamination

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Jan 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Software Development
  1. LLMs do better on tasks related to older data compared to newer data. This means they might struggle with recent information.
  2. Training data can affect how well LLMs perform in certain tasks. If they have seen examples before, they can do better than if it's completely new.
  3. Task contamination can create a false impression of an LLM's abilities. It can seem like they are good at new tasks, but they might have already learned similar ones during training.

LLM-Generated Self-Explanations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Dec 23
🕹 Technology AI Research Machine Learning Natural Language Processing Data Analysis Software Engineering
  1. LLMs can make predictions and explain how they arrived at those predictions. This helps in understanding their reasoning better.
  2. Using a 'Chain of Thoughts' method can improve LLMs' ability to solve complex tasks, especially in areas like math and sentiment analysis.
  3. There's a need for better ways to evaluate the explanations given by LLMs because current methods may not accurately determine which explanations are effective.

A Comprehensive Survey of Large Language Models (LLMs)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Dec 23
🕹 Technology AI Research Language Models Natural Language Processing Machine Learning Open Source
  1. The number of research papers on large language models (LLMs) has surged significantly, rising from about one per day to nearly nine since 2019. This shows a growing interest in understanding these models.
  2. Three important skills of LLMs are in-context learning, following instructions, and step-by-step reasoning. These abilities help models perform better on various tasks.
  3. Open-source LLMs, like Meta's LLaMA, have made it easier for researchers to customize and grow these models, leading to more innovation in the field.

Data Delivery To Large Language Models [Updated]

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Dec 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing Prompt engineering
  1. Every effective AI strategy needs a solid data strategy that includes data discovery, design, development, and delivery.
  2. At inference, providing the right context and relevant data is crucial to help language models produce accurate responses.
  3. Training models involves two key phases: meta-training for foundational knowledge and meta-learning for fine-tuning on specific tasks.

OpenAI String Tokenisation Explained

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Nov 23
🕹 Technology AI Models Natural Language Processing Machine Learning Programming
  1. Tokenisation is the process of breaking down text into smaller pieces called tokens, which can be converted back to the original text easily. This makes it useful for understanding and processing language.
  2. Different OpenAI models use different methods for tokenising text, meaning the same input can result in different token counts across models. It’s important to know which model you are using.
  3. Using tokenisation can shorten the text length in terms of bytes, making the input more efficient. On average, each token takes up about four bytes, which helps models learn better.

Self-Refine Is An Iterative Refinement Loop For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI Machine Learning Natural Language Processing Software Development Data science
  1. Self-Refine improves LLM output without needing extra training data. It does this by refining the output through feedback in a loop.
  2. The approach mimics how humans recheck their work to find better ways to express ideas, like improving an email draft or optimizing code.
  3. Quality of results gets better with more iterations, but it's important to balance this with potential delays and costs. Stronger models produce better refinements.

Context-Aware Meta-Learning For Foundation Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology AI Machine Learning Natural Language Processing Computer Vision Data science
  1. Large Language Models can learn quickly from little information during use, without needing extra training. This makes them very flexible in understanding and generating text.
  2. Currently, images don't learn as easily as text when it comes to recognizing new things on the spot. Improving this could allow visual models to learn like language models do.
  3. The new method called Context-Aware Meta-Learning helps visual models learn new concepts right away without extra setup. This can lead to exciting new applications that connect text and images better.

A New Prompting Approach From DeepMind Called Analogical Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Oct 23
🕹 Technology AI Machine Learning Deep Learning Data science Natural Language Processing
  1. DeepMind's Analogical Prompting helps language models recall similar past problems to solve new ones. This way, models can learn from existing knowledge without needing specific examples every time.
  2. This approach allows models to create their own relevant examples, reducing the need for human labeling and making the problem-solving process more efficient.
  3. By generating tailored examples, DeepMind's method improves the accuracy of solutions while also simplifying the training process for the models.

Agents, LLMs & Multihop Question Answering

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Apr 23
🕹 Technology AI Machine Learning Natural Language Processing Software Development Data science
  1. Agents can use different tools based on user requests. This gives them the flexibility to respond to questions that don't fit a typical sequence.
  2. Prompt chaining involves linking prompts together to create a more complex response. However, it can struggle with unexpected user queries.
  3. For better responses, it's important for an Agent to have clear instructions on which tool to use. Fine-tuning these instructions can improve how well the Agent answers questions.

Multi-Label Text Classification With Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Cloud Computing Data science Natural Language Processing
  1. Google Cloud Vertex AI allows for multi-label text classification, which means multiple tags can be assigned to a document. This helps in better organizing and processing text data.
  2. Training a model on Vertex AI can take a long time, especially with large datasets. For example, using nearly 12,000 training items can take over four hours to complete.
  3. The system's interface for managing training data and labels can be complex and a bit confusing. This makes it harder to easily update and manage the training data.

Three Ways In Which Whisper Is Advancing ChatGPT

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science
  1. Speech to text has unique challenges, like disfluencies that happen when people talk. These differences can help improve how ChatGPT understands and processes voice input.
  2. Whisper can provide ChatGPT with access to lots of audio data. This means it can learn from a wider variety of information, which can make responses better.
  3. The future of AI models includes using different types of data, not just text. This shift towards multi-modal models means ChatGPT can eventually handle audio, images, and more, making it more versatile.

What Are Realistic GPT-4 Size Expectations?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Feb 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Data science Natural Language Processing
  1. GPT-4 is likely to have around 1 trillion parameters, which is much smaller than the rumored 100 trillion. This is based on how language models have grown over time.
  2. Experts suggest that it's not just about the number of parameters. The quality of training data is equally important for improving performance in language models.
  3. There is a limited supply of high-quality language data. If better data sources don’t emerge, the growth of model sizes may slow down significantly.

Use relative dates and to help users learn your UI

CommandBlogue • 0 implied HN points • 20 Mar 24
🕹 Technology User Experience User Interface Product Design Software Development Natural Language Processing
  1. Using relative dates makes it easier for users to understand and interact with a user interface. For example, saying 'next Thursday' is more natural than giving a specific date.
  2. People think about time differently than computers do. They often use relative terms, so designs should accommodate that way of thinking.
  3. Date pickers should be simple and consistent with other input methods. Changing how users input information can frustrate them and make the experience less enjoyable.

[in case you missed it] Data Science Weekly - Issue 461

Data Science Weekly Newsletter • 0 implied HN points • 25 Sep 22
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Database Management
  1. NLP is a growing field, but using it effectively is still a challenge for many. People are eager to learn how to make NLP useful in their work.
  2. Curating social media accounts can be a rewarding experience. It helps to connect with a community and share insights in fun ways.
  3. Generative AI can boost productivity and creativity significantly. It has the potential to create a lot of economic value by making workers faster and more effective.

[in case you missed it] Data Science Weekly - Issue 440

Data Science Weekly Newsletter • 0 implied HN points • 08 May 22
🕹 Technology Data science Machine Learning AI Software Development Natural Language Processing
  1. Using advanced tools like JAX and Julia can improve machine learning and scientific computing.
  2. Feedback from people can help train language models to produce better results, avoiding offensive or incorrect content.
  3. Data visualizations can provide deep insights, but they should encourage thoughtful reflection, not just clarity.

[in case you missed it] Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 0 implied HN points • 02 May 20
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Software Development
  1. Tornado plots are a unique way to display time series data, showing how values change over time in a more dynamic way. They help visualize trends in a more engaging format.
  2. An open-source chatbot named Blender, developed by Facebook, is designed to be more human-like in conversations. It is the largest chatbot model available and can be used by other researchers.
  3. The use of machine learning (ML) for optimizing chip design is becoming important as hardware needs to keep up with advancing technology. It could help speed up the design process significantly.

How To Build a Japanese Pronunciation Checker With Python and Wit.ai

Curious Devs Corner • 0 implied HN points • 02 Sep 24
🕹 Technology Software Development Programming Languages Natural Language Processing Open Source Artificial Intelligence
  1. You can build a Japanese pronunciation checker using Python and Wit.ai. It's a fun way to practice speaking Japanese and get instant feedback.
  2. The app works by recording your voice and comparing it to a list of Japanese words you want to learn. If the app recognizes your speech correctly, your pronunciation is good.
  3. You can customize this tool for other languages too, making it a great project for anyone wanting to improve their language skills.

Book Review: AI-Powered Search

Talking to Computers: The Email • 0 implied HN points • 08 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Information Retrieval User Experience
  1. AI is changing how search works, moving towards using machine learning to improve results based on user feedback and interactions. This means less manual work and more personalized, efficient searches.
  2. Natural language processing helps search engines understand context and synonyms, making it easier to find relevant information. Understanding language structure allows for better handling of queries.
  3. Learning to rank is a powerful tool for improving search results based on user behavior, but it needs quality data to be effective. Without the right data, the improvements may not be as impactful as expected.

Synthetic dataset generation techniques: generating custom sentence similarity data

machinelearninglibrarian • 0 implied HN points • 23 May 24
🕹 Technology Artificial Intelligence Data science Machine Learning Natural Language Processing Synthetic Data
  1. Large Language Models (LLMs) can help create synthetic datasets for training models, especially where there's a lack of real data. This approach makes it easier to gather specific information needed for tasks like text classification.
  2. Generating sentence similarity data helps in comparing how alike two sentences are. This is useful in areas like information retrieval and clustering.
  3. A structured approach to generating data can improve the quality and relevance of the data produced. Using prompts to control the output can help generate more accurate results for specific training needs.

Synthetic dataset generation techniques: Self-Instruct

machinelearninglibrarian • 0 implied HN points • 15 May 24
🕹 Technology Machine Learning Artificial Intelligence Data generation Natural Language Processing Computational linguistics
  1. Self-Instruct helps create large sets of instructional data by using language models to generate instructions from initial examples. This saves a lot of time compared to writing everything by hand.
  2. The process involves generating new instructions from a seed dataset, filtering them, and ensuring diversity to avoid repetitive prompts. This way, the dataset expands effectively.
  3. The method is widely adopted in both research and practical applications, showing that using machine-generated data can improve instruction-following models without extensive manual input.

Using LLMs to Improve Information Retrieval

m3 | music, medicine, machine learning • 0 implied HN points • 13 Jun 24
🕹 Technology AI Information Retrieval Machine Learning Natural Language Processing Computing
  1. Using LLMs can help improve how we understand what users want from an information search. This means better matching user questions to actual retrieval queries.
  2. Having experience in a specific field helps shape these systems to give better results. It's about knowing the context in which information will be used.
  3. By combining LLMs with domain knowledge, we can create smarter queries that fetch the right info. This makes the whole retrieval process more effective.