Researchers Develop ‘TiCoder’ Framework For Code Generation Using User Feedback With 90.4% Consistency To User Intent

One of the key drivers of the recent success of powerful pretrained large language models (LLMs) in natural language processing is the model’s capacity to automatically generate code based on informal natural language prompts. However, because natural human language can frequently be ambiguous, LLMs have trouble writing code that accurately captures user intent. Taking a […]

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