Category: Machine Learning
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Temperature Parameter for Controlling AI Randomness
The Temperature parameter is a crucial setting used in generative AI models, such as large language models (LLMs), to influence the randomness and perceived creativity of the generated output. It directly affects the probability distribution of potential next words. Understanding the Basics What the Temperature Value Does In Practical Terms Using the sentence “The cat sat on…
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Probability Threshold for Top-p (Nucleus) Sampling
The “Probability Threshold for Top-p (Nucleus) Sampling” is a parameter used in generative AI models, like large language models (LLMs), to control the randomness and creativity of the output text. Here’s a breakdown of what it does: Understanding the Basics What the Threshold Value Does In Practical Terms Imagine you’re asking the model to complete…
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Cross-Model Circuit Analysis: Gemini vs. Gemma Comparison Framework
1. Introduction Understanding the similarities and differences in how different large language models represent and prioritize brand information can provide crucial insights for developing robust, transferable brand positioning strategies. This framework outlines a systematic approach for comparative circuit analysis between Google’s Gemini and Gemma model families, with the goal of identifying universal brand-relevant circuits and…
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Neural Circuit Analysis Framework for Brand Mention Optimization
Leveraging Open-Weight Models for Mechanistic Brand Positioning 1. Introduction While our previous methodology treated language models as black boxes, open-weight models like Gemma 3 Instruct provide unprecedented opportunities for direct observation and manipulation of internal model mechanics. This framework extends our previous methodology by incorporating direct neural circuit analysis, allowing for precise identification and targeting…
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Strategic Brand Positioning in LLMs: A Methodological Framework for Prompt Engineering and Model Behavior Analysis
Abstract This paper presents a novel methodological framework for systematically analyzing and optimizing the conditions under which large language models (LLMs) generate favorable brand mentions. By employing a structured probing technique that examines prompt variations, completion thresholds, and linguistic pivot points, this research establishes a replicable process for identifying high-confidence prompting patterns. The methodology enables…
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AlexNet: The Deep Learning Breakthrough That Reshaped Google’s AI Strategy
When Google, in collaboration with the Computer History Museum, open-sourced the original AlexNet source code, it marked a significant moment in the history of artificial intelligence. AlexNet was more than just an academic breakthrough; it was the tipping point that launched deep learning into mainstream AI research and reshaped the future of companies like Google.…
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Beyond Rank Tracking: Analyzing Brand Perceptions Through Language Model Association Networks
This post is based on the codebase and specifications for AI Rank, an AI visibility and rank tracking framework developed by DEJAN AI team: https://airank.dejan.ai/ Abstract: Traditional SEO has long relied on rank tracking as a primary metric of online visibility. However, modern search engines, increasingly driven by large language models (LLMs), are evolving beyond…
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Teaching AI Models to Be Better Search Engines: A New Approach to Training Data
A recent patent application* reveals an innovative method for training AI models to become more effective at understanding and answering human queries. The approach tackles a fundamental challenge in modern search technology: how to teach AI systems to truly understand what people are looking for, rather than just matching keywords. The Core Innovation The traditional…
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Self-Supervised Quantized Representation for KG-LLM Integration
Paper: https://arxiv.org/pdf/2501.18119 This paper proposes a method called Self-Supervised Quantized Representation (SSQR) for seamlessly integrating Knowledge Graphs (KGs) with Large Language Models (LLMs). The key idea is to compress the structural and semantic information of entities in KGs into discrete codes (like tokens in natural language) that can be directly input into LLMs. Here’s a…
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Introducing VecZip: Embedding Compression Algorithm
Embeddings are vital for representing complex data in machine learning, enabling models to perform tasks such as natural language understanding and image recognition. However, these embeddings can be massive in size, creating challenges for storage, processing, and transmission. At DEJAN AI, we’ve developed VecZip, a novel approach to address this issue, and reduce the file size…