Evaluating LLM Compression: Balancing Efficiency, Trustworthiness, and Ethics in AI-Language Model Development

LLMs have shown remarkable capabilities but are often too large for consumer devices. Smaller models are trained alongside larger ones, or compression techniques are applied to make them more efficient. While compressing models can significantly speed up inference without sacrificing much performance, the effectiveness of smaller models varies across different trust dimensions. Some studies suggest […]

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