Qwen3 Model Scaling from 0.6B to 235B Parameters and Robustness Against Distractor Noise in Needle-in-a-Haystack Retrieval

Abstract

Abstract: Large Language Models (LLMs) are increasingly becoming the preferred foundation platforms for many Natural Language Processing tasks such as Machine Translation, owing to their quality often comparable to or better than task-specific models, and the simplicity of specifying the task through natural language instructions or in-context examples. Their generality, however, opens them up to subversion by end users who may embed into their requests instructions that cause the model to behave in unauthorized and possibly unsafe ways. In this work we study these Prompt Injection Attacks (PIAs) on mul

Research Question

Does increasing Qwen3 model size from 0.6B to 235B parameters improve robustness against distractor noise in needle-in-a-haystack retrieval tasks beyond the superficial capabilities measured by standard NIAH tests?

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