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Fine-tuning LLMs explained with human social learning analogy

By PulseAugur Editorial · [1 sources] ·

Fine-tuning in large language models can be understood through a human analogy of learning social behavior. Prompt engineering is akin to temporary instructions given before an event, while instruction tuning involves teaching through examples and feedback. Fine-tuning, however, represents a deeper, internal change, similar to how a person internalizes social norms over time, leading to more natural and nuanced responses without constant external guidance. AI

IMPACT Provides an intuitive understanding of LLM fine-tuning by relating it to human socialization and character development.

RANK_REASON The item uses an analogy to explain a technical concept rather than reporting on a new development.

Read on Medium — fine-tuning tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Fine-tuning LLMs explained with human social learning analogy

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COVERAGE [1]

  1. Medium — fine-tuning tag TIER_1 English(EN) · Twishha Soni ·

    Fine-Tuning Explained: A Simple Human Analogy

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://twishhasoni.medium.com/fine-tuning-explained-a-simple-human-analogy-5653f528fbc9?source=rss------fine_tuning-5"><img src="https://cdn-images-1.medium.com/max/1488/1*wTsGpRro0zIZjYfybgu4JQ.png" width="1488…

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