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William & Mary
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MLLMs exhibit a staggering 80.22% bias towards incorrect outputs when faced with repeated UI patterns, revealing a critical flaw in their code generation capabilities.
English isn't always the best choice for generating high-quality code鈥攍anguage bias significantly impacts LLM performance across programming tasks.
AQLM outperforms full-precision baselines in code generation, while QuIP# falters under complex prompts, revealing critical trade-offs in quantization strategies.
