arXiv:2606.17609v1 Announce Type: new Abstract: Compressing large language models reduces memory use and inference cost, but it can also create failures that standard benchmarks miss. A pruned model may still perform well on multiple-choice evaluations, yet fail to answer the same question in open generation. We ask what pruning changes: does it erase the correct answer, or does it make the answer harder to produce as the top output? We study this question with multilingual question answering, tracking the same questions before and after pruning. We find a benchmark illusion. Under high-sparsi
The increasing focus on deploying resource-constrained LLMs and the need to optimize their performance make understanding the precise effects of compression critical.
This research reveals a critical blind spot in current LLM evaluation methods, suggesting that models deemed performant by standard benchmarks may actually be functionally impaired.
The criteria for evaluating and confidently deploying pruned or compressed LLMs must now become more sophisticated, moving beyond simple multiple-choice performance.
- · Researchers developing advanced LLM evaluation methodologies
- · Companies investing in robust, multi-faceted LLM testing
- · Developers of un-pruned or less-aggressively pruned models
- · Companies relying solely on multiple-choice benchmarks for LLM quality
- · Early adopters of highly-pruned LLMs for generative tasks
- · Developers of aggressive LLM compression techniques without validation
There will be a renewed scrutiny of LLM benchmark design and the methodologies for evaluating compressed models.
This could lead to a temporary slowdown in the adoption of highly compressed models for critical generative applications as reliability concerns emerge.
New techniques may arise to preserve generative capabilities during pruning or to reliably detect and characterize such 'benchmark illusions'.
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Read at arXiv cs.CL