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I recently came across a thread on Reddit discussing the issue of companies running out of human-generated training data for large language models (LLMs). A year ago, there were frequent news stories highlighting this as a looming problem with training data “running out” in the near future. The discussion then pivoted to potential solutions such as using synthetic data.
- The use of synthetic data was proposed as an alternative, but it was noted that this approach had its own set of challenges; specifically, it could cause issues for the final model and potentially pollute the outputs.
- Despite these initial concerns, there hasn’t been much recent discussion or news about whether this issue has been resolved. The absence of new information suggests either that the problem may have been mitigated or is not a significant concern at present.
- The continued improvement in LLMs without explicit mention of this issue indicates that perhaps the concerns were overblown, or solutions to these challenges have been implemented effectively.
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– The use of synthetic data as a solution was initially suggested but faced criticism due to potential issues with model performance.
– There hasn’t been much recent discussion about whether the issue has been resolved, which may indicate that it might no longer be a significant problem or that solutions have been implemented effectively.
– The ongoing improvement in LLMs without mentioning this specific issue suggests either that the concerns were overstated or that practical solutions have addressed these challenges.
Originally published at reddit.com. Curated by AI Maestro.
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