AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization (DPO) provides a stable framework for preference learning, its effectiveness in personalized settings critically depends on how preference p...
RAG systems commonly retrieve a fixed number of documents (top-k) to ground generation, but this static approach is brittle: simple queries suffer over-retrieval (adding noise and cost) while complex queries are under-retrieved, causing recall failures that cascade into incorrect answers. Motivated by the question of h...
Large language models (LLMs) have improved document information extraction, but turning extracted facts into relational databases remains fundamentally difficult. The challenge is that extraction is local to text, whereas database construction must satisfy global semantics defined by schemas, keys, and integrity cons...
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