Category: llm
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Classifier-free guidance for LLMs performance enhancing
Classifier-free guidance for LLMs performance enhancing Classifier-Free Guidance for LLMs Performance Enhancing Check and improve classifier-free guidance for text generation large language models. While participating in NeurIPS 2024 Competitions track I was awarded the second prize in the LLM Privacy challenge. The solution I had used classifier-free guidance (CFG). I noticed that with high CFG guidance…
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An Agentic Approach to Reducing LLM Hallucinations
An Agentic Approach to Reducing LLM Hallucinations Simple techniques to alleviate LLM hallucinations using LangGraph Photo by Greg Rakozy on Unsplash If you’ve worked with LLMs, you know they can sometimes hallucinate. This means they generate text that’s either nonsensical or contradicts the input data. It’s a common issue that can hurts the reliability of LLM-powered…
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A New Approach to AI Safety: Layer Enhanced Classification (LEC)
A New Approach to AI Safety: Layer Enhanced Classification (LEC) LEC surpasses best in class models, like GPT-4o, by combining the efficiency of a ML classifier with the language understanding of an LLM Imagine sitting in a boardroom, discussing the most transformative technology of our time — artificial intelligence — and realizing we’re riding a rocket with no reliable safety…
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From Prototype to Production: Enhancing LLM Accuracy
From Prototype to Production: Enhancing LLM Accuracy Implementing evaluation frameworks to optimize accuracy in real-world applications Image created by DALL-E 3 Building a prototype for an LLM application is surprisingly straightforward. You can often create a functional first version within just a few hours. This initial prototype will likely provide results that look legitimate and be…
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Classifier-Free Guidance in LLMs Safety — NeurIPS 2024 Challenge Experience
Classifier-Free Guidance in LLMs Safety — NeurIPS 2024 Challenge Experience Classifier-Free Guidance in LLMs Safety — NeurIPS 2024 Challenge Experience This article briefly describes NeurIPS 2024 LLM-PC submission that was awarded the second prize — the approach to effective LLM unlearning without any retaining dataset. This is achieved through the formulation of the unlearning task as an alignment problem with the…
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Structured LLM Output Using Ollama
Structured LLM Output Using Ollama Control your model responses effectively Continue reading on Towards Data Science » Thomas Reid Go to original source
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How to Use Structured Generation for LLM-as-a-Judge Evaluations
How to Use Structured Generation for LLM-as-a-Judge Evaluations Structured generation is fundamental to building complex, multi-step reasoning agents in LLM evaluations — especially for open source models Source: Generated with SDXL 1.0 Disclosure: I am a maintainer of Opik, one of the open source projects used later in this article. For the past few months, I’ve been working on LLM-based…
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How to Evaluate Multilingual LLMs With Global-MMLU
How to Evaluate Multilingual LLMs With Global-MMLU Evaluation of language-specific LLM accuracy on the global Massive Multitask Language Understanding benchmark in Python Continue reading on Towards Data Science » Dr. Leon Eversberg Go to original source
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Chat with Your Images using Multimodal LLMs
Chat with Your Images using Multimodal LLMs Chat with Your Images Using Llama 3.2-Vision Multimodal LLMs Learn how to build Llama 3.2-Vision locally in a chat-like mode, and explore its Multimodal skills on a Colab notebook Annotated image by author. Original image by Pixabay. Introduction The integration of vision capabilities with Large Language Models (LLMs) is revolutionizing…
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How to Build a General-Purpose LLM Agent
How to Build a General-Purpose LLM Agent A Step-by-Step Guide High-level Overview of an LLM Agent. (Image by author) Why build a general-purpose agent? Because it’s an excellent tool to prototype your use cases and lays the groundwork for designing your own custom agentic architecture. Before we dive in, let’s quickly introduce LLM agents. Feel free…
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Google Gemini Is Entering the Advent of Code Challenge
Google Gemini Is Entering the Advent of Code Challenge An open-source project to explore the capabilities and limitations of LLMs on coding challenges Image by author (created with Flux 1.1 Pro) What is this about? If 2024 taught us anything in the realm of Generative AI, then it is that coding is one of the most promising…
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How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs?
How Did Open Food Facts Fix OCR-Extracted Ingredients Using Open-Source LLMs? Delve into an end-to-end Machine Learning project to improve the quality of the Open Food Facts database Image generated with Flux1 Open Food Facts’ purpose is to create the largest open-source food database in the world. To this day, it has collected over 3 millions products…