Summary
A study testing 14 diverse LLMs, ranging from 0.6B to 123B parameters (including Llama 3.1, Mistral, and Qwen3), revealed a significant degradation in instruction-following performance when users employed hostile prompts. This negative effect, approximately a 10% drop for 7-8B models, was consistent across various architectures, quantization tiers, and routing methods. Although the impact attenuates with increasing model scale, it remains a significant issue even for the largest models tested.
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