What are the famous LLM models?

Mon, Dec 2023
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LLM, in the context of law education, stands for Master of Laws. However, in the context of machine learning and artificial intelligence, LLM could refer to Language Model (LM). Here are some famous language models: 1. BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT is a transformer-based machine learning technique for natural language processing pre-training. It can understand the context of a word by looking at its surroundings in both directions. 2. GPT (Generative Pretrained Transformer): OpenAI's GPT models are another set of powerful transformer-based language models. The latest version, GPT-3, has 175 billion machine learning parameters and can generate human-like text. 3. RoBERTa: A robustly optimized BERT approach developed by Facebook AI, RoBERTa modifies key hyperparameters in BERT to improve its performance. 4. T5 (Text-to-Text Transfer Transformer): Introduced by Google Research team, T5 reframes all NLP tasks into a unified text-to-text format to simplify the process of applying a model across different tasks. 5. XLNet: Proposed by researchers at Google Brain and Carnegie Mellon University, XLNet is an extension of the Transformer-XL model and outperforms BERT on several NLP benchmarks. 6. ALBERT: A Lite version of BERT developed by Google Research that reduces redundancy in the model and improves parameter-efficiency. 7. ELECTRA: Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) is another model developed by Google Research which is more efficient than traditional masked language modeling methods like BERT. 8. Transformer-XL: This model introduces a recurrence mechanism to Transformers that enables them to handle longer-term dependency, making it particularly effective for tasks like text generation. 9. ERNIE: Developed by Baidu, ERNIE (Enhanced Representation through kNowledge IntEgration) is a continual pre-training framework that builds and learns incrementally by pre-training tasks through multi-task learning. 10. DistilBERT: A smaller, faster, cheaper version of BERT developed by Hugging Face team. It retains 95% of BERT's performance while being 60% smaller and 60% faster. These models have been instrumental in advancing the field of natural language processing and understanding.
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