Can Large Language Models Really Recognize Your Name?
📰 ArXiv cs.AI
Large Language Models can struggle to recognize human names due to linguistic ambiguities, which has implications for privacy pipelines
Action Steps
- Test LLMs on datasets with diverse human names to evaluate their performance
- Analyze the linguistic ambiguities that lead to errors in name recognition
- Configure LLMs to handle out-of-vocabulary names and edge cases
- Apply techniques such as named entity recognition and part-of-speech tagging to improve name recognition accuracy
- Compare the performance of different LLMs on name recognition tasks to identify the most effective models
Who Needs to Know This
Data scientists and AI engineers working on privacy pipelines and natural language processing tasks can benefit from understanding the limitations of LLMs in recognizing human names
Key Insight
💡 LLMs are not foolproof in recognizing human names, and their limitations can have significant implications for privacy pipelines
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🚨 LLMs can struggle to recognize human names due to linguistic ambiguities 🚨
Key Takeaways
Large Language Models can struggle to recognize human names due to linguistic ambiguities, which has implications for privacy pipelines
Full Article
Title: Can Large Language Models Really Recognize Your Name?
Abstract:
arXiv:2505.14549v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used in privacy pipelines to detect and remedy sensitive data leakage. These solutions often rely on the premise that LLMs can reliably recognize human names, one of the most important categories of personally identifiable information (PII). In this paper, we reveal how LLMs can consistently mishandle broad classes of human names even in short text snippets due to ambiguous linguistic cu
Abstract:
arXiv:2505.14549v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used in privacy pipelines to detect and remedy sensitive data leakage. These solutions often rely on the premise that LLMs can reliably recognize human names, one of the most important categories of personally identifiable information (PII). In this paper, we reveal how LLMs can consistently mishandle broad classes of human names even in short text snippets due to ambiguous linguistic cu
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