Aravind Putrevu
Devrel
學習、聯繫、建置
Microsoft Reactor
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學習、聯繫、建置
Microsoft Reactor
加入 Microsoft Reactor 並與開發人員即時互動
準備好開始使用 AI 和最新技術嗎？ Microsoft Reactor 提供活動、訓練和社群資源，協助開發人員、企業家和初創公司建置 AI 技術等等。 加入我們！
Přejít zpět
Improving Large Language Model by Systematically Improving its Data
26 února, 2024 | 12:00 odp. - 1:00 odp. (UTC) Koordinovaný univerzální čas
- Formát:
Živý stream
Téma: Datová Věda a strojové učení
Jazyk: Angličtina
Labeled data powers AI/ML in the enterprise, but real-world datasets have been found to contain between 7-50% annotation errors. Imperfectly labelled text data hampers ML models' training (and evaluation) across tasks like intent recognition, entity recognition, and sequence generation. Although pretrained LLMs are equipped with a lot of world knowledge, their performance is adversely affected by noisy training data (as noted by OpenAI).
In this talk, we illustrate data-centric techniques to mitigate the effect of label noise without changing any code related to model architecture, hyperparameters, or training. These data quality improvement techniques should thus remain applicable even for future advanced LLMs like GPT-10.
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