I’m Dominik and this is my personal website. I will be talking about various topics including: machine learning, startups and other technology topics.
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The dramatic improvements in Large Language Model performance have largely been driven by scaling - increasing model size, training data, and computational resources. This scaling has led to the emergence of unexpected capabilities, challenging our understanding of intelligence and learning.
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The way prompts are crafted and processed by Large Language Models reveals fundamental differences between human and machine-generated content. Understanding prompt trajectories - how prompts evolve and are interpreted through the AI system - provides insights into human-AI interaction patterns and optimization opportunities.
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As Large Language Models become more powerful, the ability to reliably steer their behavior becomes increasingly important. LLM steering encompasses various techniques to guide model outputs toward desired outcomes while maintaining coherence and usefulness.
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As Large Language Models become central to information retrieval and content generation, a new form of Search Engine Optimization (SEO) has emerged. LLM SEO focuses on optimizing content to be effectively indexed, ranked, and retrieved by AI systems, rather than traditional search engines.
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Large Language Models are vulnerable to various forms of poisoning attacks that can compromise their integrity, reliability, and safety. Understanding these attack vectors and developing robust defenses is crucial for maintaining trust in AI systems.
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Large Language Models (LLMs) have shown remarkable capabilities in generating human-like text, but they are prone to producing “hallucinations” - confident assertions that are factually incorrect or nonsensical. This post explores the phenomenon of hallucinations in LLMs, examining their underlying causes, mitigation strategies, and attempts at mechanistic interpretation.
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