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Why I Ditched spaCy in Favor of LLMs for Natural Language Processing
- Convention keyword-based NLP solutions like spaCy are difficult to keep up when organizational needs change and inquiries become more complex which gave the rise to Large Language Models (LLMs).
- Recent breakthroughs in LLMs like Qwen, OpenAI, and LLaMA variants have given us a new toolkit.
- LLMs have extraordinary capacity for context, ambiguity, and nuance understanding.
- LLMs give freedom to more dynamically interpret intent and allow us to make inferences regarding semantic meaning.
- LLMs provide predictability in output using language modeling, training, finetuning, and prompt engineering techniques.
- Creating precise and modular prompts, the higher accuracy and improvement in adaptability in intent detection system are achievable.
- There was a significant improvement over the previous attempts using spaCy, with a 70% accuracy in the first LLM-based prototype.
- Intent detection is not done by a single prompt; instead, an agentic workflow breaks the logic into multiple steps to debug and improve incrementally.
- The intent detection module is the linchpin in the pipeline, without accurate source selection, the rest of the pipeline might fetch irrelevant or incomplete data.
- Shifting from spaCy to LLM-based intent detection is a game-changer, resulting in a smoother, more accurate, and scalable approach to intent detection.
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