Training large language models on overly cleaned and de-identified data can lead to models that produce synthetic or over-sanitized answers. While privacy protection is important, excessive scrubbing of input data risks removing the context, variation, and imperfections that mirror real-world language and behavior. This can result in models that are coherent but disconnected from the realities they are meant to represent. AI
IMPACT Over-sanitization of LLM training data may lead to models that lack real-world context and produce less useful outputs.
RANK_REASON The item discusses the potential negative consequences of over-cleaning training data for LLMs, offering an opinion on data sanitization practices.
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