mailitics

Tag: language

  • A path to natural language through tokenisation and transformers

    A path to natural language through tokenisation and transformers arXiv:2601.03368v1 Announce Type: cross Abstract: Natural languages exhibit striking regularities in their statistical structure, including notably the emergence of Zipf’s and Heaps’ laws. Despite this, it remains broadly unclear how these properties relate to the modern tokenisation schemes used in contemporary transformer models. In this note,…

  • Natural Language Visualization and the Future of Data Analysis and Presentation

    Natural Language Visualization and the Future of Data Analysis and Presentation Will conversational interaction replace SQL queries, KPI reports, and dashboards? The post Natural Language Visualization and the Future of Data Analysis and Presentation appeared first on Towards Data Science. Michal Szudejko Go to original source

  • Bringing Vision-Language Intelligence to RAG with ColPali

    Bringing Vision-Language Intelligence to RAG with ColPali Unlocking the value of non-textual contents in your knowledge base The post Bringing Vision-Language Intelligence to RAG with ColPali appeared first on Towards Data Science. Julian Yip Go to original source

  • What Makes a Language Look Like Itself?

    What Makes a Language Look Like Itself? How simple statistics reveal the visual fingerprints of 20 languages The post What Makes a Language Look Like Itself? appeared first on Towards Data Science. Kenneth McCarthy Go to original source

  • Using Vision Language Models to Process Millions of Documents

    Using Vision Language Models to Process Millions of Documents Learn how to effectively apply vision language models to problem solving The post Using Vision Language Models to Process Millions of Documents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • (Im)possibility of Automated Hallucination Detection in Large Language Models

    (Im)possibility of Automated Hallucination Detection in Large Language Models arXiv:2504.17004v1 Announce Type: cross Abstract: Is automated hallucination detection possible? In this work, we introduce a theoretical framework to analyze the feasibility of automatically detecting hallucinations produced by large language models (LLMs). Inspired by the classical Gold-Angluin framework for language identification and its recent adaptation to…

  • Agentic AI: Single vs Multi-Agent Systems

    Agentic AI: Single vs Multi-Agent Systems We’ve seen this shift the last few years from building rigid programming systems to natural language-driven workflows, all made possible with more advanced large language models. One of the interesting areas into these Agentic Ai systems is the difference between building a single versus multi-agent workflow, or perhaps the…

  • LLaDA: The Diffusion Model That Could Redefine Language Generation

    LLaDA: The Diffusion Model That Could Redefine Language Generation Introduction What if we could make language models think more like humans? Instead of writing one word at a time, what if they could sketch out their thoughts first, and gradually refine them? This is exactly what Large Language Diffusion Models (LLaDA) introduces: a different approach to…

  • An Overview of Large Language Models for Statisticians

    An Overview of Large Language Models for Statisticians arXiv:2502.17814v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures,…

  • How to Measure the Reliability of a Large Language Model’s Response

    How to Measure the Reliability of a Large Language Model’s Response The basic principle of Large Language Models (LLMs) is very simple: to predict the next word (or token) in a sequence of words based on statistical patterns in their training data. However, this seemingly simple capability turns out to be incredibly sophisticated when it…

  • Beyond Causal Language Modeling

    Beyond Causal Language Modeling A deep dive into “Not All Tokens Are What You Need for Pretraining” Introduction A few days ago, I had the chance to present at a local reading group that focused on some of the most exciting and insightful papers from NeurIPS 2024. As a presenter, I selected a paper titled…

  • Segmenting Water in Satellite Images Using Paligemma

    Segmenting Water in Satellite Images Using Paligemma Some insights on using Google’s latest Vision Language Model Hutt Lagoon, Australia. Depending on the season, time of day, and cloud coverage, this lake changes from red to pink or purple. Source: Google Maps. Multimodal models are architectures that simultaneously integrate and process different data types, such as text, images,…

  • Ranking of Large Language Model with Nonparametric Prompts

    Ranking of Large Language Model with Nonparametric Prompts arXiv:2412.05506v1 Announce Type: new Abstract: We consider the inference for the ranking of large language models (LLMs). Alignment arises as a big challenge to mitigate hallucinations in the use of LLMs. Ranking LLMs has been shown as a well-performing tool to improve alignment based on the best-of-$N$…

  • 169  |  Data Conversations with Vidya Setlur

    169  |  Data Conversations with Vidya Setlur We have Vidya Setlur on the show to talk about the role language, and natural language processing (NLP) play in data visualization and analytics. Vidya is the director of research at Tableau and has a background in natural language processing and visualization. She is one of the main drivers behind…