Paul Prinsloo’s Post

The world of artificial intelligence has been revolutionized by the advent of Large Language Models (LLMs). These models, such as GPT-4 and its successors, are more than just advanced text generators; they are sophisticated information-theoretic data compression engines. A recent analysis on LLMs delves deep into their technical underpinnings and explores how they harness mathematical principles from information theory to compress vast volumes of textual data into concise, coherent, and contextually relevant responses. This explains their extraordinary capabilities in natural language understanding and generation, making them versatile tools for language tasks, chatbots, content generation, and translation services. It's worth noting that these models also serve as data compression engines, albeit of a unique kind - information-theoretic data compressors. To learn more about the fascinating world of LLMs, check out this insightful article. #ArtificialIntelligence #NaturalLanguageProcessing #DataCompression #InformationTheory

Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing but often produce inconsistent results. Retrieval-Augmented Generation (RAG) offers a solution by grounding LLMs with external data, ensuring more reliable outputs. However, evaluating RAG systems presents unique challenges. Our latest blog post breaks down key considerations for evaluating RAG-based systems, including: 🎯 Ensuring relevance and accuracy of retrieved documents 🔍 Evaluating faithfulness and contextual relevance of responses 📃 Managing multi-document integration and conflicting data ⚖ Balancing scalability, performance, and costs https://lnkd.in/d54Yf5ei And stay tuned for a detailed tutorial on building and evaluating RAG applications later this week! #AI #DataScience #RAG #MachineLearning #LLM #Evaluation #TechInsights

What is Chain-of-Thought (CoT) Prompting? Examples & Benefits - In recent years, large language models (LLMs) have made remarkable strides in their ability to understand and generate human-like text. These models, such as OpenAI's GPT and Anthropic's Claude, have demonstrated impressive performance on a wide range of natural language processing tasks. However, when it comes to complex reasoning tasks that require multiple steps of logical thinking, traditional prompting methods often fall short. This is where Chain-of-Thought (CoT) prompting comes into play, offering a powerful prompt engineering technique to improve the reasoning capabilities of large language models. Key Takeaways CoT prompting enhances reasoning capabilities by generating intermediate steps. It breaks […] - https://lnkd.in/ehbAiS7s

What is Chain-of-Thought (CoT) Prompting? Examples & Benefits - In recent years, large language models (LLMs) have made remarkable strides in their ability to understand and generate human-like text. These models, such as OpenAI's GPT and Anthropic's Claude, have demonstrated impressive performance on a wide range of natural language processing tasks. However, when it comes to complex reasoning tasks that require multiple steps of logical thinking, traditional prompting methods often fall short. This is where Chain-of-Thought (CoT) prompting comes into play, offering a powerful prompt engineering technique to improve the reasoning capabilities of large language models. Key Takeaways CoT prompting enhances reasoning capabilities by generating intermediate steps. It breaks […] - https://lnkd.in/ehbAiS7s

Artificial Intelligence LLMs v Knowledge Graphs. Yin and Yang, Masculine and Feminine get married Well, the feminine is finally getting its due in AI. Now lets get women in there to authenticate it. Men's internal intellectual feminine traits are not enough. That said, Quantum Computers are operating, not on 1s or 0s but 1s AND 0s .. they are literally non-binary, A mix of probabilities of potential waiting to manifest.. in the meantime might as well have them carry some computations on them.

What is Chain-of-Thought (CoT) Prompting? Examples & Benefits - In recent years, large language models (LLMs) have made remarkable strides in their ability to understand and generate human-like text. These models, such as OpenAI's GPT and Anthropic's Claude, have demonstrated impressive performance on a wide range of natural language processing tasks. However, when it comes to complex reasoning tasks that require multiple steps of logical thinking, traditional prompting methods often fall short. This is where Chain-of-Thought (CoT) prompting comes into play, offering a powerful prompt engineering technique to improve the reasoning capabilities of large language models. Key Takeaways CoT prompting enhances reasoning capabilities by generating intermediate steps. It breaks […] - https://lnkd.in/ehbAiS7s

What is Chain-of-Thought (CoT) Prompting? Examples & Benefits - In recent years, large language models (LLMs) have made remarkable strides in their ability to understand and generate human-like text. These models, such as OpenAI's GPT and Anthropic's Claude, have demonstrated impressive performance on a wide range of natural language processing tasks. However, when it comes to complex reasoning tasks that require multiple steps of logical thinking, traditional prompting methods often fall short. This is where Chain-of-Thought (CoT) prompting comes into play, offering a powerful prompt engineering technique to improve the reasoning capabilities of large language models. Key Takeaways CoT prompting enhances reasoning capabilities by generating intermediate steps. It breaks […] - https://lnkd.in/ehbAiS7s

Interesting findings from “LMExplainer: a Knowledge-Enhanced Explainer for Language Models” Article link: https://lnkd.in/gZrn9HSg Large language models (LLMs) like GPT-4 have taken the world by storm with their impressive natural language processing abilities. Nevertheless, their lack of transparency and explainability are huge factors that result in people feeling apprehensive about their use in real-world applications, especially in critical fields like healthcare or education. There exists a myriad of approaches to address this, but many solutions either neglect the reasoning process or fail to fully interpret the inner workings of LLMs. The knowledge-enhanced explainer, LMExplainer, was introduced in this article as an alternative solution that was flexible for use with any LLM. For each question and set of multiple-choice answers, a knowledge graph was constructed to encapsulate elements in the given question, answers and in other knowledge connecting them. Then, graph convolutional networks (GCNs) and graph attention networks (GATs) were employed to go through the graph and identify important connections that led to the answer. Explanations for arrival at the final answer could be generated by tracing the model’s reasoning path through the graph. In particular, the model was asked to give two explanations: why it chose a specific answer and why it did not select the other choices. In this way, LMExplainer managed to outperform other LM+KG (language model and knowledge graph) methods on question answering problems. On top of providing better, clearer explanations for a model’s choices and the reasoning behind them, LMExplainer showed that this approach also improved a model’s performance. Reference: Chen, Z., Singh, A. K., & Sra, M. (2023). LMExplainer: a Knowledge-Enhanced Explainer for Language Models (Version 2). arXiv. DOI: 10.48550/ARXIV.2303.16537 #knowledgegraphs #llms #reasoning

#Quantization is a promising technique to reduce the computational and memory overhead of LLMs. "Large language models (#LLMs), like the infamous #ChatGPT, have achieved impressive performance on a variety of natural language processing tasks, such as machine translation, text summarization, and question-answering. They have changed the way we communicate with computers and the way we do our tasks. LLMs have emerged as transformative entities, pushing the boundaries of natural language understanding and generation. Among these, ChatGPT stands as a remarkable example, representing a class of LLMs designed to interact with users in conversational contexts. These models are the result of extensive training on extremely large text datasets. This gives them the ability to comprehend and generate human-like text. However, these models are computationally and memory-intensive, which limits their practical deployment. As the name suggests, these models are large; when we mean large, we mean it. The most recent open-source LLM, LLaMa2 from Meta, contains around 70 billion parameters. Reducing these requirements is an important step in making them more practical. Quantization is a promising technique to reduce the computational and memory overhead of LLMs. There are two main ways to do quantization – post-training quantization (PTQ) and quantization-aware training (QAT). While QAT offers competitive accuracy, it’s prohibitively expensive in terms of both computation and time. Therefore, PTQ has become the go-to method for many quantization efforts. Existing PTQ techniques, like weight-only and weight-activation quantization, have achieved significant reductions in memory consumption and computational overhead. However, they tend to struggle with low-bit quantization, which is crucial for efficient deployment. This performance degradation in low-bit quantization is primarily due to the reliance on handcrafted quantization parameters, leading to suboptimal results. OmniQuant is a novel quantization technique for LLMs that achieves state-of-the-art performance across various quantization scenarios, particularly in low-bit settings, while preserving the time and data efficiency of PTQ." https://lnkd.in/g7TzwkPp

Human minds are complex interplay between system 1 and system 2 thinking. Perhaps the right model to integrate KS + LLM may be the first step towards AGI.... yet I doubt true compassion, ethical consideration, and strategic planning can be 100 percent done by AI. I am not saying human do these complex jobs well, but I am imagining that perhaps AI can augment human in these areas. #AI #AGI