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Improving GPT-3 with human feedback

Benched.ai Editorial Team

Fine-tuning GPT-3 with user feedback for accuracy, domain expertise and lower costs using data labeling

Fine-tuning lets teams customise GPT-3 for specialised tasks. By collecting prompt and completion pairs, developers train a new model that performs better than manual prompting. This approach shortens prompts, reduces latency and aligns outputs with domain requirements. Open-source tools and OpenAI’s API simplify the process but data quality remains critical.

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