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

A study evaluates the trustworthiness of compressed LLMs using different compression techniques. Quantization is found to be more effective than pruning, with moderate bit-range quantization enhancing trust dimensions like ethics and fairness. Extreme quantization poses risks to trustworthiness.