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Data Labeling for LLMs: The Key to Safer and More Effective AI Models
Large language models (LLMs) are rapidly growing in number, capability, and popularity—from ChatGPT bringing them into the mainstream to DeepSeek now making waves in the tech world with its low-cost, high-performance features—alongside many other influential models. Trained on a massive amount of data, LLMs can generate text, write computer code, interpret images and audio, and… Continue reading Data Labeling for LLMs: The Key to Safer and More Effective AI Models The post Data Labeling for LLMs: The Key to Safer and More Effective AI Models appeared first on Cogitotech.
Large language models (LLMs) are rapidly growing in number, capability, and popularity—from ChatGPT bringing them into the mainstream to DeepSeek now making waves in the tech world with its low-cost, high-performance features—alongside many other influential models. Trained on a massive amount of data, LLMs can generate text, write computer code, interpret images and audio, and solve math problems. However, despite their impressive human-like intelligence, they are far from infallible, often producing incorrect, misleading, or even harmful outputs. This necessitates human oversight to ensure their safety and reliability. This article explores the role of data labeling for LLMs and how it bridges the gap between the potential of Gen AI models and their reliability and applicability in real-world scenarios.
The post Data Labeling for LLMs: The Key to Safer and More Effective AI Models appeared first on Cogitotech.
