Partner Contribution • • 3 minute read
Partner POV | The Double-Edged Sword of Data: Starving and Poisoning Large Language Models (LLMs)
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This article was written by Dr. Melvin Greer, Intel Fellow and Chief Data Scientist, Americas, Intel Corporation.
Large Language Models (LLMs) have become powerful tools, capable of generating human-quality text, translating languages, and writing creative content. However, their effectiveness hinges on the data quality they are trained on. Two significant threats can arise - data starvation and data poisoning - significantly impacting the trustworthiness of AI solutions.
Imagine an LLM trained on a limited dataset of children's books. While it might excel at crafting whimsical stories, it would struggle with complex topics or factual accuracy. This is the essence of data starvation. An LLM fed an insufficient amount of data, or data lacking in diversity, will exhibit limitations in its capabilities.
The impact of data starvation is multifaceted:
Data poisoning occurs when malicious actors deliberately inject biased or incorrect data into the training dataset. This can have disastrous consequences, manipulating the LLM's outputs to serve a specific agenda.
The risks of data poisoning are severe:
Organizations can safeguard against data starvation and poisoning by implementing a multi-pronged approach:
Data starvation and poisoning directly impact the trustworthiness of AI solutions. Inaccurate, biased, or easily manipulated outputs erode user confidence and hinder the broader adoption of AI. When users cannot rely on the information generated by LLMs, they become hesitant to engage with AI-powered services.
By actively mitigating these risks, organizations can ensure the responsible development and deployment of LLMs. Trustworthy AI solutions built on diverse, high-quality data will ultimately lead to a future where humans and machines collaborate effectively for the betterment of society.