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Unlocking AI Insights: Key Takeaways from Working with Large Language Models
The advancements in artificial intelligence, particularly with large language models (LLMs), have had a significant impact on various sectors. As Semantic Visions is in the process of exploring LLMs capabilities and integrating these models into our workflows, we have encountered challenges, surprises, and many learning opportunities. In this article, we will share key takeaways from our experiences, offering insights for both technical enthusiasts and businesses looking to leverage these AI tools. In our exploration, Semantic Vision’s machine learning team focused on five core areas using LLMs’ capabilities: Topic Classification, Named Entity Recognition (NER), Text Paraphrasing, Knowledge Graph Population, and Victim/Aggressor Role Labeling.
Key Takeaways
- Topic Classification:
- Large Language Model (LLM) fine-tuning is now an almost trivial task with OpenAI tools.
- Name Entity Recognition:
- While Large Language Models excel in many tasks, specialized models designed and optimized for specific applications can outperform them. Named Entity Recognition is one such example, where specialized models are, for the time being, better.
- Text Paraphrasing Findings:
- Text paraphrasing and summarization with GPT models is very impressive and already suitable for production. GPT 3.5 Davinci is slightly better than ChatGPT 3.5.
- Knowledge Graph Population Findings:
- Using LLMs, transforming unstructured data into knowledge graphs has become more feasible than ever.
- Victim/Aggressor Role Labeling:
- Fine-tuned Llama2 model with 13 billion parameters provided on average 10% better accuracy on the Victim/Aggressor task than the much larger ChatGPT 3.5 model, with 175 billion parameters.
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