Tell me about your most interesting project in the field of NLP and LLM, including architecture and solution evaluation.

In one of my projects involving NLP and LLM, I worked on an automatic text analysis and generation system for customer support. The architecture included several components:

• Data preprocessing: cleaning and normalization of texts.
• A transformer-based model (e.g., GPT-like architecture) for response generation.
• A quality assessment module using perplexity and BLEU metrics.

For training the model, large dialogue corpora and specialized datasets were used. As a result, the accuracy of responses was improved, and the processing time for customer requests was reduced. The solution was evaluated both quantitatively (quality metrics) and qualitatively (user feedback).