Ars Technica - All content 10月24日 06:01
低质量数据对LLM影响研究
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本文探讨了低质量数据对大型语言模型(LLM)的影响,研究人员发现持续使用低质量数据进行预训练可能导致LLM的认知能力下降,类似于人类的‘大脑退化’。

On the surface, it seems obvious that training an LLM with “high quality” data will lead to better performance than feeding it any old “low quality” junk you can find. Now, a group of researchers is attempting to quantify just how much this kind of low quality data can cause an LLM to experience effects akin to human “brain rot.”

For a pre-print paper published this month, the researchers from Texas A&M, the University of Texas, and Purdue University drew inspiration from existing research showing how humans who consume “large volumes of trivial and unchallenging online content” can develop problems with attention, memory, and social cognition. That led them to what they’re calling the “LLM brain rot hypothesis,” summed up as the idea that “continual pre-training on junk web text induces lasting cognitive decline in LLMs.”

Figuring out what counts as “junk web text” and what counts as “quality content” is far from a simple or fully objective process, of course. But the researchers used a few different metrics to tease a “junk dataset” and “control dataset” from HuggingFace’s corpus of 100 million tweets.

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LLM 低质量数据 认知能力 大脑退化 数据预训练
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