100K Tokens | Why it Mater
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What are advantage of 100K token size in LLM mode? 100k token size in LLM (large language models) like GPT-3 have the following advantages: Greater representational capacity - With more parameters, the model can learn more complex representations of language and knowledge. This allows it to generate more accurate and coherent text. Better long-term coherence - With a larger context window of 100k tokens, the model can maintain coherence over longer timescales in the generated text. Shorter models tend to lose coherence faster. Ability to handle more complex prompts - The larger model size allows it to understand and generate text for more complex and nuanced prompts. Better factual and semantic consistency - With more context and parameters, the model has a better grasp of facts, entities and their relationships. This helps it generate more factually and semantically consistent text. Ability to handle more diverse tasks - The large size enables the model to be distilled and fine-tuned for more diverse NLP tasks like summarization, question answering, translation, etc. However, there are also some limitations of very large LLM sizes like 100k tokens: Higher costs - Both in terms of training costs and inference latency/costs in production. Higher carbon footprint - Due to the large computational requirements. Difficulty in optimization and training - Very large models are difficult to train efficiently. Increased risk of bias and toxicity - Due to being trained on a very large and diverse web corpus. So in summary, while 100k token sizes offer significant advantages over smaller LLM sizes, organizations need to weigh the trade-offs based on their specific needs and constraints. The optimal size depends on the intended use cases and performance requirements. |