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Quantum Leap in Language Models: QLoRA’s Impact on Email Privacy

Explore cutting-edge PII detection with QLoRA LLMs at MLDS 2024, showcasing superior performance in email security.

Bengaluru played host to India’s premier generative AI conference, the Machine Learning Developers Summit (MLDS) 2024, where luminaries from the field showcased their groundbreaking insights. Among them was Chinmay Prakash, a Google-certified Machine Learning Engineer and NLP Data Scientist at Genpact. Armed with a rich academic background from BITS Pilani, Goa Campus, Chinmay specializes in developing cutting-edge models and building scalable machine learning systems, particularly in the realms of Generative AI and Natural Language Processing (NLP).

A Comparative Analysis with BERT and GPT3.5

Chinmay captivated the audience with his talk on “PII Detection in Emails through QLoRA Fine-tuned LLMs.” He began by addressing the significance of personally identifiable information (PII) and its critical role in the age of pervasive text analytics. As emails serve as repositories of sensitive data, the challenge lies in ensuring that machine learning models don’t inadvertently memorize or expose this information. Chinmay emphasized the tediousness of manual PII masking and delved into the existing tools before introducing the crux of his talk—the application of QLoRA Fine-tuned Language Models (LLMs).

Challenges in PII Detection

Chinmay outlined the challenges in PII detection, highlighting the need for accurate systems to safeguard information. Emails, containing details like email IDs, IP addresses, and phone numbers, demand robust solutions to prevent data breaches and uphold privacy standards.

Introduction to QLoRA Fine-tuned LLMs

The heart of Chinmay’s presentation lay in introducing QLoRA Fine-tuned LLMs. Leveraging Quantum Language Representation Architecture, these models offer a unique approach to language representation, enabling nuanced understanding and context awareness in NLP tasks.

Comparative Analysis: QLoRA vs. BERT vs. GPT-3.5

Chinmay meticulously compared QLoRA Fine-tuned LLMs with heavyweight models like BERT and GPT-3.5. The analysis covered accuracy, computational efficiency, and scalability. Real-world use cases and benchmarks illustrated the advantages of QLoRA in enhancing PII detection in emails.

Scalability and Efficiency

A key highlight was QLoRA’s scalability and efficiency, surpassing existing models while maintaining computational efficiency. Chinmay showcased how these models are not only cutting-edge but also practical for real-world applications.

Practical Insights and Future Implications

Closing his talk, Chinmay shared practical insights gained from implementing QLoRA Fine-tuned LLMs at Genpact. He addressed the challenges of PII detection, emphasizing the model’s potential in enhancing data security and privacy. Additionally, he discussed the future implications of this technology, hinting at broader applications beyond PII detection.


In conclusion, Chinmay Prakash’s talk at MLDS 2024 shed light on the evolving landscape of PII detection. The comparison with established models and the practical insights shared offered a glimpse into the future of secure communication and information protection. As the audience left the session, they carried with them a deeper understanding of the advancements in generative AI, thanks to the expertise and revelations unveiled by Chinmay Prakash.

Picture of Shreepradha Hegde

Shreepradha Hegde

Shreepradha is an accomplished Associate Lead Consultant at AIM, showcasing expertise in AI and data science, specifically Generative AI. With a wealth of experience, she has consistently demonstrated exceptional skills in leveraging advanced technologies to drive innovation and insightful solutions. Shreepradha's dedication and strategic mindset have made her a valuable asset in the ever-evolving landscape of artificial intelligence and data science.

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