Large-Scale Text Corpus Deduplication and Dataset Enhancement

• Developed and deployed text corpus deduplication using a suffix array algorithm on MapReduce, boosting assessor F1-score from 0.77 to 0.82.
• Trained a classifier to improve benchmark coverage, enhancing dataset relevance for pretraining tasks.
• Created a dataset augmentation pipeline with Back Translation, increasing pretraining robustness and generalization.
• Enhanced document parsing quality, resulting in 3% faster model convergence and improved resource efficiency.

• Implemented processing of all basic data types from Clang AST to equivalent constructions in Eolang.
• Developed a mechanism for translating multidimensional arrays to Eolang.
• Developed a mechanism for translating Enum types to Eolang.

• Engineered a SOTA multi-document summarization model for news aggregation, achieving an 87% human acceptance rate with less than 1% critical errors.
• Designed automated news timelines for evolving events, resulting in a 2.8% increase in content depth and a 1.4% boost in user time spent.
• Optimized ranking algorithms with a CTR prediction model, increasing daily active users by 4.3% and user engagement by 2.1%.
• Created a novel similarity scoring formula, improving F1-score from 0.91 to 0.95 for news clustering.

In the corporate sector, text data flows swiftly, emanating from diverse sources in various formats, destined for multiple endpoints and actions. This continuous stream of data necessitates management through structuring, making it easy to interpret and analyse, and ultimately directing it to the appropriate user or applying the insights obtained. I utilised several large language models (LLMs) streamline and enhance this process:

• A question-answering LLM, enabling users to inquire about the content of each document within the flow.
• A summarization LLM, offering a concise overview of each document.
• An NLP model for sentiment analysis, determining the sentiment of each document.
• An NLP model that calculates an absolute similarity score among all documents.
• Clustering, which groups similar documents together and simultaneously identifies outlier documents.

• Built and maintained backend modules for a rendering SaaS using Python.

• Developed a chatbot to automate communication between technical support and clients.

• Managed and optimised MariaDB and PostgreSQL databases for internal tools.