𝐊𝐞𝐲 𝐋𝐋𝐌 𝐓𝐞𝐫𝐦𝐬 𝐭𝐨 𝐊𝐧𝐨𝐰 𝐢𝐧 2024: 𝐀 𝐐𝐮𝐢𝐜𝐤 𝐆𝐮𝐢𝐝𝐞

1. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫 𝐌𝐨𝐝𝐞𝐥

- The foundation of modern NLP, transformers process text efficiently by handling multiple parts of a sentence simultaneously.

• Use Case: Use a transformer model to create a translation app that quickly and accurately translates conversations in real-time.

2. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠

- Fine-tuning adapts a pre-trained model to excel at a specific task by training it on a smaller, specialized dataset.

• Use Case: Fine-tune a language model on your company's customer support emails to improve automated responses.

3. 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠

- Embeddings turn words into numerical vectors, capturing their meanings and relationships for machine processing.

• Use Case: Use embeddings to improve the accuracy of a recommendation system by better understanding product reviews.

4. 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧

- Tokenization breaks down text into smaller pieces like words or subwords, making it easier for machines to process.

• Use Case: Tokenize user inputs to prepare them for a chatbot that answers customer queries.

5. 𝐏𝐫𝐞-𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠

- Pre-training involves teaching a model general language patterns using a large dataset before fine-tuning it for specific tasks.

• Use Case: Pre-train a language model on diverse text data, then fine-tune it for medical record analysis.

6. 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦

- Attention mechanisms allow models to focus on the most relevant parts of the input, improving understanding and accuracy.

• Use Case: Use attention mechanisms in a document summarization tool to highlight key information in lengthy reports.

7. 𝐁𝐄𝐑𝐓

- BERT is a model that understands the context of words by looking at the surrounding text, making it very accurate.

• Use Case: Implement BERT to enhance search functionality in an online store by better interpreting user queries.

8. 𝐆𝐏𝐓

- GPT excels at generating human-like text, making it ideal for content creation tasks.

• Use Case: Use GPT to draft engaging blog posts or social media content automatically.

9. 𝐏𝐞𝐫𝐩𝐥𝐞𝐱𝐢𝐭𝐲

- Perplexity measures how well a language model predicts text; lower perplexity indicates better performance.

• Use Case: Evaluate different chatbot models by comparing their perplexity scores to ensure coherent and relevant responses.