LLM (Large Language Model) - Java Programmatic Universe

9 (C)- LLM benchmarks and leaderboards

9 (B)- Human evaluation and user studies

9 (A)- Automatic evaluation metrics (BLEU, ROUGE, METEOR)

8 (C)- AI governance and ethical frameworks

8 (B)- Privacy and security considerations

8 (A)- Bias and fairness in LLMs

7 (E)- Code generation and programming assistance

7 (D)- Content creation and creative writing

7 (C)- Question answering and information retrieval

7 (B)- Text generation and summarization

7 (A)- Chatbots and conversational AI

6 (C)- Load balancing and scaling LLM systems

6 (B)- Serving LLMs in production environments

6 (A)- Model compression and quantization

5 (C)- Prompt engineering and few-shot learning

5 (B)- Fine-tuning LLMs for specific tasks (text generation, summarization, question answering)

5 (A)- Pre-training techniques (self-supervised learning, masked language modeling)

4 (C)- TensorFlow and PyTorch for LLMs

4 (B)- Anthropic’s models and APIs

4 (A)- Hugging Face Transformers

3 (E)- LLaMA, Anthropic’s models, and other open-source LLMs

3 (D)- GPT-3 and its variants (GPT-J, GPT-Neo)

3 (C)- T5 (Text-to-Text Transfer Transformer)

3 (B)- Bidirectional Encoder Representations from Transformers (BERT)

3 (A)- Generative Pre-trained Transformer (GPT)

2 (E)- Transformer Architecture

2 (D)- Attention Mechanisms

2 (C)- Convolutional Neural Networks for NLP

2 (B)- Recurrent Neural Networks (RNNs, LSTMs, GRUs)

2 (A)- Feedforward Neural Networks