The hottest Natural Language Substack posts right now

And their main takeaways
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Top Technology Topics

Unearthing Datasets Preparation for LLM

The Beep • 19 implied HN points • 11 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language
  1. Good datasets are really important for training large language models (LLMs). If the data isn't well prepared, the model won't perform well.
  2. To prepare a dataset, you need to gather data, clean it up, and then convert it into a format the model can understand. Each step is crucial.
  3. While training LLMs, it's important to think about issues like data bias and privacy. This can affect how well the model works and who it might unfairly impact.

Key Components to Understand the LLM Models

The Beep • 19 implied HN points • 07 Jan 24
🕹 Technology AI Models Natural Language Machine Learning Neural Networks Data processing
  1. Large language models (LLMs) like Llama 2 and GPT-3 use transformer architecture to process and generate text. This helps them understand and predict words based on previous context.
  2. Emergent abilities in LLMs allow them to learn new tasks with just a few examples. This means they can adapt quickly without needing extensive training.
  3. Techniques like Sliding Window Attention help LLMs manage long texts more efficiently by breaking them into smaller parts, making it easier to focus on relevant information.

Active Prompting with Chain-of-Thought for Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Jan 24
🕹 Technology AI Machine Learning Data science Natural Language Automation
  1. AI can help improve language models by using a four-step process: estimating uncertainty, selecting uncertain questions, annotating them, and making final inferences. This helps ensure better answers.
  2. Using human annotations along with AI makes the training data clearer and reduces confusion. It allows us to focus on the most important information for the models.
  3. Companies can benefit from this approach by streamlining how they handle data. It promotes a more organized way of discovering, designing, and developing data.

Chain-Of-Knowledge Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 22 Nov 23
🕹 Technology AI NLP Machine Learning Data science Natural Language
  1. Chain-Of-Knowledge (CoK) prompting is a useful technique for complex reasoning tasks. It helps make AI responses more accurate by using structured facts.
  2. Creating effective prompts using CoK requires careful construction of evidence and may involve human input. This is important for ensuring the quality and reliability of the information AI generates.
  3. The CoK approach aims to reduce errors or 'hallucinations' in AI responses. It offers a more transparent way to build prompts and enhances the overall reasoning ability of AI systems.
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Great, now the machines start to understand me

Klement on Investing • 1 implied HN point • 06 Dec 24
🕹 Technology AI Machine Learning Natural Language Computing Research
  1. Generative AI has made big strides in understanding language, but it still struggles with things like irony and context. These are important parts of how people communicate every day.
  2. Recent studies show that chatGPT-4 is getting much better at understanding complex human interactions, sometimes even matching or surpassing human understanding. This shows how AI is evolving.
  3. AI still has weaknesses; for example, it can struggle with recognizing social mistakes people make in conversations. Unlike chatGPT, another model called LLaMA2 did better at this specific task.

Newsletter #7: Advanced Prompt Engineering

Decoding Coding • 19 implied HN points • 30 Mar 23
🕹 Technology AI Machine Learning Programming Natural Language Data science
  1. Zero-shot prompting lets a model answer questions without examples. It's useful when there's no data to guide the model.
  2. Few-shot prompting gives the model a few examples to improve its answers. This helps the model understand the context better.
  3. Chain-of-thought prompting breaks down complex problems into steps. It helps the model reason through tasks more effectively.

Making Peace with LLM Non-determinism

The Finest Tuners • 5 HN points • 07 Apr 24
🕹 Technology Machine Learning Artificial Intelligence Natural Language Software Engineering
  1. Non-determinism in language models can be frustrating because you can't always expect the same output each time you input the same prompt. This unpredictability often stems from the way language itself works.
  2. You can reduce some of this unpredictability by using techniques like seeding and selecting better models. These methods help control how outputs are generated and make them more consistent.
  3. Understanding that language is inherently complex can help you see the random outputs as part of the model's nature, not just flaws. Embracing this chaos can lead to surprising and interesting results.

Data Science Weekly - Issue 440

Data Science Weekly Newsletter • 19 implied HN points • 05 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Natural Language
  1. Meta AI is sharing a big language model, OPT-175B, to help others learn about new technology. This model has 175 billion parameters and is based on publicly available data.
  2. Handling harmful text in data science is a tricky issue. Researchers are looking for ways to address this challenge while still making progress in natural language processing.
  3. There are many resources and courses available for learning data science and machine learning. These include guides for using Python and R, plus access to various data visualization tools.

Data Science Weekly - Issue 418

Data Science Weekly Newsletter • 19 implied HN points • 25 Nov 21
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Natural Language
  1. Understanding data strategy is crucial for companies. Many invest in data, but few create a data-driven culture.
  2. Deep learning can help with smart, autonomous systems, but caution is needed in safety-critical applications.
  3. Tools like Retool make it easier for teams to build applications on their data without needing extensive coding skills.

Data Science Weekly - Issue 298

Data Science Weekly Newsletter • 19 implied HN points • 08 Aug 19
🕹 Technology AI Data science Machine Learning Software Development Natural Language
  1. AI is becoming a part of dating apps, helping users find potential matches by analyzing their conversations.
  2. Natural Language Processing is evolving, with new trends emerging from major conferences like ACL 2019.
  3. Tools like Teraport simplify the process of building data pipelines, making it easier to manage data for machine learning projects.

Data Science Weekly - Issue 28

Data Science Weekly Newsletter • 19 implied HN points • 05 Jun 14
🕹 Technology Data science Machine Learning Predictive Analytics Natural Language Software Development
  1. Machine Learning can be used to analyze emotions in real-time. Tools like NLTK and ZMQ make it easier to develop services for this purpose.
  2. Apache Spark is gaining popularity as more companies see its benefits for processing large datasets. This trend is fueled by improvements in its components and an expanding community.
  3. Text analysis can significantly improve stock price prediction accuracy. It has been shown that including text data can enhance predictions by over 10% compared to traditional methods.

Retrieval Augmented Fine-Tuning (RAFT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Mar 24
🕹 Technology AI Machine Learning Natural Language Data science Computing
  1. RAFT helps language models focus on useful documents while answering questions and ignore irrelevant ones. This means the model can provide more accurate and relevant responses.
  2. RAFT combines the benefits of supervised fine-tuning with retrieval-augmented generation. This allows the model to learn from both specific documents and broader patterns in data.
  3. The way data is prepared for training in RAFT is really important. It ensures that each training example has a question, related documents, and a clear answer.

Large Language Models Excel At In-Context Learning (ICL)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Mar 24
🕹 Technology AI Machine Learning Natural Language Data science Generative AI
  1. Large Language Models (LLMs) can learn better when given contextual information, which helps them be more accurate and reduce mistakes.
  2. Retrieval-augmented generation (RAG) is a useful method because it allows models to customize responses without needing a lot of extra training.
  3. Even with good context, LLMs can still create some incorrect responses, showing that they sometimes mix up information in a believable way.

Time-Aware Adaptive RAG (TA-ARE)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Mar 24
🕹 Technology AI Machine Learning Natural Language Data processing Software Development
  1. Time-Aware Adaptive RAG (TA-ARE) helps decide when it's necessary to retrieve extra information for answering questions, making the process more efficient.
  2. Adaptive retrieval is better than standard methods because it only retrieves information when needed, reducing unnecessary costs in using resources.
  3. The study suggests that understanding the timing of questions can improve how large language models respond, making them more capable without needing extra training.

Leveraging LLM In-Context Learning Abilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Natural Language Software Development Data science
  1. Large Language Models (LLMs) learn best when given specific context in their prompts. They use this context to generate accurate answers instead of relying solely on what they were previously trained on.
  2. Response time is very important when using LLMs, especially for conversational applications. Hosting LLMs locally can help reduce delays and save on costs.
  3. The process of breaking down complex questions into smaller ones can lead to better answers. This involves organizing thoughts and evaluating the quality of the information used to answer the questions.

Seven RAG Engineering Failure Points

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Data processing Software Development
  1. Retrieval-Augmented Generation (RAG) reduces errors in information by combining data retrieval with language models. This helps produce more accurate and relevant responses.
  2. RAG allows for better organization of data, making it easy to include specific industry-related information. This is important for tailoring responses to user needs.
  3. There are several potential failure points in RAG, such as missing context or providing incomplete answers. It's crucial to design systems that can handle these issues effectively.

Chain Of Natural Language Inference (CoNLI)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Jan 24
🕹 Technology Artificial Intelligence Natural Language Data Analysis Machine Learning Software Development
  1. There are three types of hallucinations in AI-generated text: context-free, ungrounded, and self-conflicting. Each type means there's a different way the text can be misleading.
  2. The CoNLI framework helps detect and reduce hallucinations in text responses. It can rewrite responses to improve their accuracy without needing special tuning.
  3. CoNLI works even when the user has limited control over the AI model, making it easier to ensure that the generated output aligns with correct information.

TinyStories Is A Synthetic DataSet Created With GPT-4 & Used To Train Phi-3

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 04 Jul 24
🕹 Technology AI Data science Machine Learning Natural Language Computing
  1. TinyStories is a unique dataset created using GPT-4 to train a language model called Phi-3. It focuses on generating small children's stories that are easy to understand.
  2. The dataset includes around 3,000 carefully chosen words, which are mixed to create diverse stories without repetitive content. This helps the model learn language better.
  3. Creating this kind of synthetic data allows smaller language models to perform well in simple tasks, making them useful for organizations that might not have the resources for larger models.

What Is Multi-Task Language Understanding or MMLU?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 19 Dec 23
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Multi-Task Language Understanding (MMLU) measures how well language models perform on various subjects. It uses a huge set of multiple-choice questions to test their knowledge.
  2. Though some language models like GPT-3 show improvement over random guessing, they still struggle with complex topics like ethics and law. They often don't recognize when they're wrong.
  3. Model confidence isn't a good indicator of accuracy. For example, GPT-3 can be very confident in its answers, but still be far from correct.

Five Stages Of LLM Implementation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Dec 23
🕹 Technology AI Data Development Machine Learning Natural Language
  1. Implementing LLMs (Large Language Models) changes how applications are developed. Many teams focus on building tools instead of actually using them, which creates a gap.
  2. Getting data right is vital for successful LLM implementation. Companies should look closely at their data strategy to ensure LLMs perform well, especially during real-time use.
  3. There are several stages to using LLMs effectively. Starting from design time benefits user experience by avoiding issues like high costs and slow responses when deployed.

Gemini From Google

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Dec 23
🕹 Technology AI Models Machine Learning Natural Language Cloud Computing Data processing
  1. Google's Gemini is a powerful AI that can understand and work with text, images, video, audio, and code all at once. This makes it really versatile and capable of handling different types of information.
  2. Starting December 6, 2023, Google's Bard will use a version of Gemini Pro for better reasoning and understanding. This means Bard will soon be smarter and more helpful in answering questions.
  3. Gemini has shown it can outperform human experts in language tasks. This is a significant achievement, indicating that AI is getting very close to human-like understanding in complex subjects.

As-Needed Decomposition & Planning Using Large Language Models — (ADaPT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 05 Dec 23
🕹 Technology AI Machine Learning Natural Language Automation Algorithms
  1. ADaPT is a method that breaks down complex tasks into smaller steps only when needed. This helps manage complicated tasks better.
  2. This approach uses a planner to come up with a big plan and then hands off simpler steps to another model for execution. This makes the process smoother.
  3. ADaPT adds resilience and smart logic to using language models, allowing them to handle tasks that get tricky and require adjustments along the way.

Self-Consistency For Chain-Of-Thought Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 04 Dec 23
🕹 Technology AI Language Models Data science Machine Learning Natural Language
  1. Self-consistency prompting helps improve the accuracy of language models when solving reasoning problems. It does this by generating different reasoning paths and choosing the most consistent answer.
  2. Using self-consistency can lead to better performance in various tasks, including arithmetic and common-sense reasoning. It shows clear accuracy gains across multiple language models.
  3. This approach requires careful sampling and processing of the reasoning paths to get the best final answer. It's all about making sense of the various responses to reach a clear conclusion.

ChatGPT Is One-Year Old: Are Open-Source Large Language Models Catching Up?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Dec 23
🕹 Technology AI Machine Learning Natural Language Chatbots Open Source
  1. Some open-source language models are doing better than ChatGPT in specific tasks, showing that they are improving quickly. For example, models like Lemur-70B-chat are better at certain coding tasks.
  2. The study highlights that while open-source models are catching up, GPT models like ChatGPT still excel in areas like AI safety, making them important for commercial use.
  3. Understanding the differences between raw LLMs, LLM APIs, and user interfaces is crucial, as people often mix these terms up in discussions about AI technology.

The Anatomy Of Chain-Of-Thought Prompting (CoT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Nov 23
🕹 Technology AI Machine Learning Natural Language Data science
  1. Chain-of-Thought (CoT) prompting helps large language models solve problems by breaking them down into smaller steps, just like humans do.
  2. For CoT to work well, the reasoning steps need to be ordered correctly and must be relevant to the question being asked.
  3. Even with incorrect reasoning, CoT can still perform well, showing that the overall method is more important than every single detail being perfect.

Knowledge-Driven Chain-of-Thought (KD-CoT)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Nov 23
🕹 Technology AI Machine Learning Natural Language Data science Human-computer interaction
  1. The Knowledge-Driven Chain-of-Thought (KD-CoT) helps improve how language models answer questions by using knowledge from outside sources. This means better answers for complex questions.
  2. In-Context Learning (ICL) is important for language models. It allows them to use examples and context to provide more accurate and contextually relevant responses.
  3. Researchers are focusing on making language models better by using a human-in-the-loop approach, which means humans help guide and improve the model's ability to access and use data effectively.

AgentInstruct Uses Agentic Flows To Create Synthetic Training Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Automation
  1. Microsoft is using advanced methods to create high-quality synthetic training data for language models. This helps improve the data's diversity and reduces the need for human oversight.
  2. Agentic workflows are important because they allow multiple agents to generate and refine data, making the process more efficient and effective.
  3. The approach can create large amounts of customized data from unstructured sources quickly, which is useful for enhancing AI models during different training stages.

Chain-Of-Note (CoN) Retrieval For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Nov 23
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. Chain-of-Note (CoN) helps improve how language models find and use information. It does this by sorting through different types of information to give better answers.
  2. CoN uses three types of reading notes to keep responses accurate. This means it can better handle situations where the data isn’t directly answering a question.
  3. Combining CoN with data discovery and design is important for getting reliable information. This makes sure that language models work well in different situations.

Are Emergent Abilities In LLMs Inherent Or Merely In-Context Learning?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Nov 23
🕹 Technology AI Machine Learning Natural Language Data science Prompt engineering
  1. Emergent abilities in language models (LLMs) allow them to perform well on tasks they weren't specifically trained for. This shows a level of flexibility in handling diverse challenges.
  2. These abilities might not be hidden skills but rather show how LLMs learn through in-context examples. This means that understanding context plays a big role in their performance.
  3. As LLMs get larger and better, we see improvements in their skills, often influenced by new ways of giving them instructions, indicating that these skills can expand with better training techniques.

Chain of Empathy Prompting (CoE)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Nov 23
🕹 Technology AI Natural Language Psychotherapy Language Models Human-AI Interaction
  1. Chain of Empathy Prompting (CoE) helps large language models understand and respond to human emotions better. It uses ideas from psychotherapy to recognize how a person's feelings affect what they say.
  2. Emergent abilities in language models allow them to perform unexpected tasks without being specifically trained for them. CoE is an example of how these models can develop new skills through better understanding of context.
  3. Understanding the emotional context of a conversation is crucial for effective communication between humans and AI. By recognizing feelings, AI can respond in ways that feel more supportive and understanding.

Knowledge Retrieval Via The OpenAI Playground

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Nov 23
🕹 Technology AI Machine Learning Software Development Data science Natural Language
  1. OpenAI has introduced a Retrieval Augmentation tool in its Playground. This means the assistant can now find and use information from uploaded documents to answer questions better.
  2. When users upload a file, the assistant automatically processes it. It retrieves relevant content based on what the user asks and the context needed to give an answer.
  3. This feature aims to improve the assistant's performance while offering insights for better management. More controls and flexibility will be important as users need to customize how documents are handled.

LLM Alignment, Hallucination & Misinformation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. It's important to have good data design and human supervision for large language models. This helps improve accuracy and creates better conversations.
  2. Large language models can produce different answers to the same question at different times. This means they are not always consistent.
  3. Misinformation and hallucinations can happen with these models, but we can reduce these issues by using better training and feedback methods.

Newsletter #6: Prompt Engineering

Decoding Coding • 0 implied HN points • 23 Mar 23
🕹 Technology Artificial Intelligence Natural Language Software Development Programming Data processing
  1. When using language models, the way you ask or prompt them affects the answers you get. More context often leads to better responses.
  2. You can use specific prompts to generate summaries, create text in different styles, or even test your ideas by simulating expert responses.
  3. Language models can greatly assist in coding tasks by generating templates and examples quickly, but it's important to double-check the versions of any libraries they suggest.

Newsletter #18: Vision via language

Decoding Coding • 0 implied HN points • 13 Jul 23
🕹 Technology AI Machine Learning Computer Vision Natural Language Data Analysis
  1. LENS uses large language models combined with computer vision to help computers understand images. This means computers can answer questions about visuals using language.
  2. The system has multiple components that analyze images and generate feedback. These include tagging images, describing their attributes, and creating detailed captions.
  3. This approach makes it easier for language models to handle not just images, but potentially videos and other visual inputs in the future, expanding their usefulness.

Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML

Gradient Flow • 0 implied HN points • 22 Apr 21
🕹 Technology DataOps Natural Language Benchmarks Machine Learning Funding Updates
  1. DataOps involves tools, processes, and startups that help organizations efficiently deliver AI and data products.
  2. NLU benchmarks need improvement for better model performance by focusing on better benchmark datasets.
  3. Multimodal Machine Learning and Machine Learning with Graphs are valuable resources for expanding knowledge in AI.

Programming 3.0 - Theory

Ingig • 0 implied HN points • 02 Apr 24
🕹 Technology Programming Programming Languages Natural Language Software Development
  1. Programming is transitioning to version 3.0 where computers understand abstract thinking, enabling more simple and intuitive programming.
  2. In Programming 3.0, a programming language like Plang allows defining business logic in natural language, reducing lines of code significantly while maintaining functionality and clarity.
  3. Less code often leads to improved productivity, security, fewer bugs, and increased stability in software development.

Teaching LLMs To Say “I don’t Know”

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 22 May 24
🕹 Technology Artificial Intelligence Natural Language Machine Learning Data science Reinforcement Learning
  1. Large Language Models (LLMs) often make up answers when they don't know something, which can lead to inaccuracies. Instead, it's better for them to say 'I don’t know' when faced with unfamiliar topics.
  2. LLMs can learn to give more accurate responses by being adjusted during training. They can be trained to recognize when they're unsure and respond cautiously instead of guessing.
  3. Using reinforcement learning approaches can help reduce these incorrect guesses or 'hallucinations' by teaching models to express uncertainty and limit their responses to what they truly know.

C in CRUD in Plang language

Ingig • 0 implied HN points • 27 Apr 24
🕹 Technology Programming Natural Language Database Management Artificial Intelligence Software Development
  1. Plang is an intent-based programming language designed to interpret natural language, allowing users to input information naturally instead of adjusting to a fixed data structure.
  2. With features like LLM, Plang can automate the process of converting user input into structured data, reducing the need for manual data entry and simplifying database interactions.
  3. By utilizing Plang's capabilities, developers can streamline the CRUD process by integrating natural language input and automated data structuring, enhancing user experience and data accuracy.

LLMs Excel At In-Context Learning (ICL), But What About Long In-context Learning?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Apr 24
🕹 Technology AI Machine Learning Natural Language Data science Computing
  1. Long context handling remains a challenge for large language models (LLMs). They can struggle significantly when tasks become too complex or when relevant information is in the middle of the input.
  2. LLMs perform better when key information is at the start or end of the input, but their accuracy drops when dealing with longer, more difficult tasks.
  3. Using retrieval augmented generation (RAG) can help improve performance, but it's essential to manage context effectively to avoid the 'lost in the middle' issue.