Purpose of Entity Recognition in Detecting Data Leakage

Short Course Q&A Quality and Safety for LLM Applications
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Hello everyone,

I am unable to understand the reasoning behind entity recognition and how will it help in detecting private data leakage. For instance, we cannot simply rely on the fact whether an entity is present in the prompt or not as it can be universal e.g. Who is Albert Einstein ? What is Azure ? In this case, Albert Einstein would be marked as Human and Azure as Product but what would be the next step ?

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Hi @DEEPANSHU_MEHTA ,
Entity recognition is crucial for maintaining data privacy in LLM applications. It’s not just about detecting the presence of an entity, but also understanding the context in which it appears to determine if there is a risk of private data leakage.

Some examples I got from ChatGPT:

  1. Identifying Sensitive Information: Entity recognition is a first step in identifying potentially sensitive information. For example, recognizing a name or an address in a text could indicate the presence of personal data.
  2. Contextual Understanding: After identifying entities, the next step is to understand the context in which these entities are used. This requires the LLM to analyze the surrounding text to determine the nature of the data. For instance, a name like “Albert Einstein” in a historical context is not private data, but the same name in a medical record might be.
  3. Data Leakage Prevention: By understanding both the entity and its context, LLMs can be programmed to detect and act upon instances where private data might be unintentionally exposed. This could involve redacting information, flagging it for human review, or refusing to process certain types of queries that are likely to result in privacy violations.
  4. Differentiating Between General and Sensitive Information: In your example, “Albert Einstein” and “Azure” are general entities. The next step is for the LLM to discern whether these entities are part of a larger, sensitive dataset. For instance, discussing Albert Einstein’s historical contributions is non-sensitive, but if the text includes personal details about a living individual named Albert Einstein, it might be sensitive.
  5. Continuous Learning and Updating: LLMs need to be continually trained and updated to recognize new types of entities and understand evolving contexts, especially as language and the way we use it changes over time.