The hottest AI Substack posts right now

And their main takeaways
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Top Technology Topics

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
  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
  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.

Improving Text Embeddings with LLM Generated Synthetic Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Jan 24
🕹 Technology AI
  1. Synthetic data can be used to create high-quality text embeddings without needing human-labeled data. This means you can generate lots of useful training data more easily.
  2. This study shows that it's possible to create diverse synthetic data by applying different techniques to various language and task categories. This helps improve the quality of text understanding across many languages.
  3. Using large language models like GPT-4 for generating synthetic data can save time and effort. However, it’s also important to understand the limitations and ensure data quality for the best results.

LLM Performance Over Time & Task Contamination

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Jan 24
🕹 Technology AI
  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.

OpenAI JSON Mode & Seeding

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Dec 23
🕹 Technology AI
  1. OpenAI's JSON mode doesn't ensure a specific output format, but it guarantees that the JSON will be valid. This means it will always parse without errors.
  2. Using the 'seed' parameter can help create consistent JSON structures, allowing similar inputs to produce the same output format.
  3. It's important to explicitly instruct the model to generate JSON to avoid issues; relying solely on the response format flag might lead to problems like infinite outputs.
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Prompt Pipelines

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 15 Dec 23
🕹 Technology AI
  1. Prompt pipelines are a series of steps that process requests in a structured way. They work by automatically following a set of rules to transform data and generate responses.
  2. User interaction is a key part of prompt pipelines, creating a dialog between the user and the AI application. This helps refine the results based on user input for better accuracy.
  3. These pipelines can include various stages such as keyword extraction and entity recognition, helping to analyze and interpret the user's requests more effectively.

The LangChain Ecosystem Is Expanding At A Tremendous Pace

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Dec 23
🕹 Technology AI
  1. LangChain is quickly becoming the go-to tool for developing applications using large language models (LLMs). It helps makers implement and experiment with new ideas effectively.
  2. The LangChain ecosystem now includes separate packages for different functionalities, making it easier to use and extend. These include specific tools for chains, agents, and community integrations.
  3. LangSmith offers a way to monitor and manage LLM applications, which is crucial for understanding performance and usage. This tool helps developers keep track of important metrics like costs and model accuracy.

Large Language Model Programs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Dec 23
🕹 Technology AI
  1. Using Large Language Models (LLMs) can improve many applications without needing to fine-tune them. Just accessing their capabilities as needed can work well.
  2. Breaking complex tasks into smaller steps makes it easier to manage, and LLMs can handle each part effectively. This helps in getting better results from these models.
  3. Data plays a big role in how LLMs work alongside other tools. Having clear strategies for handling data can really enhance the performance and flexibility of LLM systems.

Five Stages Of LLM Implementation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 11 Dec 23
🕹 Technology AI
  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.

OpenAI Assistants API & Python SDK

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Dec 23
🕹 Technology AI
  1. The OpenAI Assistants API helps make it easier to create virtual assistants that can handle conversations without needing prompts. This allows developers to focus more on building functionality instead of managing conversation states.
  2. While the API provides a convenient way to manage conversation history, users still incur costs for every message, which can be unclear. Understanding the token usage is essential to manage budget effectively.
  3. Creating a run in the Assistant API is asynchronous, meaning that developers need to check the status of their request until it's complete. This adds some complexity, but it does allow for better tracking of assistant performance.

OpenAI Announced 28 Models To Be Switched Off

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Dec 23
🕹 Technology AI
  1. OpenAI is shutting down 28 of its language models, and users need to switch to new models before the deadline. It's important for developers to find alternative models or consider self-hosting their solutions.
  2. Cost is a big issue with using language models; it’s usually more expensive to generate responses than to provide input. Users must monitor their token usage carefully to manage expenses.
  3. LLM Drift is a real concern, as responses from language models can change significantly over time. Continuous monitoring is needed to ensure accuracy and performance remain stable.

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
  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
  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
  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
  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.

Managing Your OpenAI Token Use

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Nov 23
🕹 Technology AI
  1. Managing OpenAI token usage is important for understanding costs. Each interaction you have with the model uses a certain number of tokens, which can add up quickly.
  2. Tokens are calculated differently depending on the model you use. It's essential to know how to convert text to tokens to estimate the cost for your specific needs.
  3. Most current implementations of LLMs focus on experimentation rather than real-time use. This means many users are not fully aware of the cost implications associated with extensive token use in their applications.

Contrastive Chain-Of-Thought Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Nov 23
🕹 Technology AI
  1. Contrastive Chain-of-Thought Prompting (CCoT) improves reasoning by using both correct and incorrect examples. This helps the model identify mistakes better.
  2. CCoT is part of a broader trend that emphasizes the importance of complex, contextual data in training models. The way data is found and formatted is crucial for success.
  3. Creating automated methods for generating examples in CCoT can enhance the learning process. By showing positive and negative instances, models can learn what to avoid.

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

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 24 Nov 23
🕹 Technology AI
  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.

Now You Can Set GPT Output To JSON Mode

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Nov 23
🕹 Technology AI
  1. You can now set the GPT model to respond in JSON format. This helps in getting structured data directly from the model.
  2. When using JSON mode, you need to set specific instructions for the model to generate valid JSON. Otherwise, it might not give you the expected output.
  3. Using a 'seed' parameter can help create consistent JSON outputs, making it easier to work with the data you receive.

The Chain-Of-X Phenomenon In LLM Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 20 Nov 23
🕹 Technology AI
  1. Chain-of-thought prompting helps large language models break down complex problems. This makes it easier for them to solve tasks step by step, just like humans do.
  2. Using chain-of-thought techniques improves the transparency of LLMs. It allows users to see how the model arrives at its answers, which can reduce mistakes.
  3. Different prompting methods, like least-to-most prompting, can be combined with chain-of-thought techniques. This flexibility can enhance the performance of models in various tasks.

Chain-Of-Note (CoN) Retrieval For LLMs

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 17 Nov 23
🕹 Technology AI
  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.

LLM Hallucination Index

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 16 Nov 23
🕹 Technology AI
  1. The LLM Hallucination Index helps measure how often AI models generate incorrect information. This is important for improving how these models perform tasks.
  2. Retrieval-Augmented Generation (RAG) significantly boosts the accuracy of AI responses by combining information retrieval and generation. It ensures the AI has better context for questions.
  3. Different AI models perform better on various tasks. OpenAI's GPT models are strong for Q&A and long-form text, while some smaller models can match their performance at a lower cost.

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
  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
  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.

OpenAI Seeding, Model Fingerprints & Log Probabilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Nov 23
🕹 Technology AI
  1. The seed parameter helps in reproducing responses from an AI by combining it with the user prompt. This means if you want the same answer again, you need to use the same seed with the same question.
  2. System fingerprints are used to track changes in the AI model or environment. If the fingerprint changes, the responses might also change, so it’s important to keep track of this along with the seed.
  3. Log probabilities will be introduced to help understand which responses the AI is likely to give. This feature can be useful for improving things like search functions and suggestions.

Now You Can Toggle OpenAI Model Determinism

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 13 Nov 23
🕹 Technology AI
  1. OpenAI now lets you control whether their model gives consistent answers to the same questions. This means if you ask it something more than once, you'll get the same answer each time.
  2. This feature is useful for testing and debugging, where you need to see the same response to know the system is working correctly.
  3. To get the same output consistently, you need to set a 'seed' number in your request. Make sure to keep the other settings the same each time you ask.

What Are OpenAI Assistant Function Tools Exactly?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 10 Nov 23
🕹 Technology AI
  1. OpenAI Assistant Function Tools help organize the output from language models. They turn casual conversation into a structured JSON format that's easier to use with external APIs.
  2. These tools allow users to create custom functions that can be called by the assistant. This means you can set up specific tasks like sending emails with the right information automatically filled in.
  3. Using Function Tools makes it simpler for developers to transform data from models. This new feature helps refine the way outputs are formatted, making them more usable for various applications.

Creating A Basic OpenAI Assistant Notebook

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Nov 23
🕹 Technology AI
  1. You can create a simple OpenAI Assistant using a few lines of code. It's easy to set up and manage right from your notebook.
  2. The assistant will need objects and threads to handle user conversations. These help store and manage message history effectively.
  3. To get responses from your assistant, you will need to implement 'runs' which check the status and allow the assistant to act on messages.

OpenAI Assistant With Retriever Tool

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Nov 23
🕹 Technology AI
  1. OpenAI assistants are like smart agents that help users by performing different tasks. They use specific tools to get the job done.
  2. The retrieval tool allows assistants to access information from various documents, enhancing their ability to answer questions accurately based on external knowledge.
  3. You can manage ongoing conversations with these assistants, allowing them to keep track of what was discussed. This helps in providing better responses over time.

Knowledge Retrieval Via The OpenAI Playground

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 08 Nov 23
🕹 Technology AI
  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.

What Is Happening To OpenAI’s Playground?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Nov 23
🕹 Technology AI
  1. The OpenAI Playground is evolving into a user-friendly dashboard that makes it easier to create and customize AI assistants without needing to code. This means users can quickly fine-tune models and build applications.
  2. OpenAI is shifting its focus towards a conversational approach, encouraging developers to create applications that enhance user experiences rather than just improve technical functionalities. Users want engaging technology that’s easy to use.
  3. New features like threads for conversations and tools for file retrieval and code execution are being added. These tools help make the AI assistants more practical and capable, allowing for a richer interaction experience.

What Are LLMs Good At & When Can LLMs Fail?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 06 Nov 23
🕹 Technology AI
  1. Large Language Models (LLMs) are great at generating clear and accurate text. They can produce sentences that make sense and are easy to read.
  2. LLMs are good at understanding language for tasks like sentiment analysis and answering questions. They can process and categorize text effectively.
  3. However, LLMs struggle with understanding complex ideas and real-world events. They can sometimes give incorrect or made-up information.

LLM Alignment, Hallucination & Misinformation

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 03 Nov 23
🕹 Technology AI
  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.

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
  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.

Self-Critique LLM Chain Using LangChain & SmartLLMChain

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology AI
  1. Using SmartLLMChain helps break down complex questions into three steps: ideation, critique, and resolution. This method can lead to better and more accurate answers.
  2. Different models can be assigned for each step of the process. This allows for tailored approaches to ideation, critique, and resolving, resulting in thorough responses.
  3. The method shows the importance of understanding how many people can work together effectively. It highlights that digging efficiency may not be simply multiplied by the number of workers involved.

A New Prompt Technique From DeepMind Called Optimisation by PROmpting (OPRO)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 02 Nov 23
🕹 Technology AI
  1. A new technique called Optimisation by PROmpting (OPRO) helps improve the performance of language models by using specific prompts. This method aims to make prompts more effective without changing the underlying models.
  2. OPRO can generate multiple prompt options at once, allowing the system to find the best one more efficiently. This strategy is helpful for solving tasks and provides better stability in results.
  3. The prompts created with OPRO can perform 8% to 50% better than those designed by humans, showing it can be more efficient in certain tasks. It's a new way to help machines understand and respond more accurately.

How Should Large Language Models Be Evaluated?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Nov 23
🕹 Technology AI
  1. Large Language Models (LLMs) should be evaluated based on their knowledge, alignment, and safety. This helps ensure they meet necessary standards.
  2. Evaluation has become more complex as LLMs can do higher-level tasks, rather than just basic language checks like syntax and vocabulary.
  3. Creating a clear taxonomy for LLM evaluation helps guide researchers and companies in assessing these models effectively.

Large Language Models & The Problem Of Abundance

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 31 Oct 23
🕹 Technology AI
  1. Chatbot development has limited tools, making it hard to create flexible and intelligent systems. Developers often start from scratch, which can slow down progress.
  2. Large Language Models (LLMs) bring many features together, but the challenge is managing their overwhelming capabilities. Instead of building from nothing, developers must learn to control and direct LLMs effectively.
  3. There is a shift towards more general LLMs that can handle various tasks, making it easier to develop comprehensive applications. New techniques are also being created to better guide LLM responses.

Prompt Editing Based On User Feedback

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology AI
  1. Understanding user intent is crucial for Large Language Models (LLMs) to provide better responses. It helps in knowing what users really want.
  2. Using feedback from users can help improve the performance of LLMs in real-time. This means users can guide the model to understand their needs better.
  3. Adding context and clarity to prompts can significantly enhance how LLMs respond. By helping the model understand the situation better, we get more accurate answers.

Context-Aware Meta-Learning For Foundation Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology AI
  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.