Tag: Model Fine-tuning (4 articles)

Hugging Face releases a new tutorial demonstrating how fine-tuning multimodal embedding models can yield performance far surpassing general-purpose large models in specific domains (like visual document retrieval), even outperforming models with 4x its parameters.

A bilingual LLM trained with semantic IDs as vocabulary tokens can recommend items and be steered through natural conversation.

Replace random hash IDs with semantic tokens so LLMs can natively understand items and enable conversational recommendations.

This article explores the phenomenon of extrinsic hallucinations in large language models, analyzing their causes and detection methods, and proposes effective strategies to reduce hallucinations while emphasizing the risks of knowledge updates.