What are the key differences in the data preparation process between fine-tuning a GPT model for text summarization versus code generation?

While both text summarization and code generation involve fine-tuning a GPT model, the data preparation process differs significantly due to the distinct characteristics of the input and output data. *Data Structure and Format:For text summarization, the data consists of long-form text documents paired with their corresponding summaries. The documents can be in various formats (e.g., articles, reports, web pages), but the key is to have a clear and concise summary for each document. For code generation, the data consists of natural language descriptions paired with their corresponding code snippets. The code snippets need to be syntactically correct and executable. *Data Cleaning and Preprocessing:For text summarization, data cleaning involves removing irrelevant characters, handling special characters, and correcting spelling and grammar errors. Stop words (common words like 'the', 'a', 'is') might be removed or retained depending on the summarization approach. For code generation, data cleaning is more focused on ensuring code syntax correctness, removing comments (or strategically using them), and standardizing code formatting (e.g., indentation, spacing). *Tokenization and Vocabulary:For text summarization, the tokenization process is relatively straightforward, using standard techniques like word-piece tokenization. The vocabulary is typically based on the entire text corpus. For code generation, tokenization requires handling code-specific elements like keywords, operators, and variable names. The vocabulary needs to include these code-specific tokens, and special tokenization techniques might be used to preserve code structure. *Data Augmentation:For text summarization, data augmentation techniques can involve paraphrasing the input documents or summaries to create new training examples. For code generation, data augmentation is more complex and can involve generating different but equivalent code snippets or modifying the natural language descriptions. *Data Balancing:For text summarization, data balancing might involve ensuring a diverse range of document lengths and topics in the training data. For code generation, data balancing needs to consider the distribution of different programming languages, code complexity levels, and types of tasks. *Input-Output Pairing:For text summarization, the input-output pairs are typically created by manually writing summaries for existing documents or by using existing datasets of summarized articles. For code generation, the input-output pairs can be created by manually writing code snippets for given descriptions or by extracting code and comments from existing code repositories. A key difference is the need for executable code for code generation, requiring careful validation and testing of the generated code snippets. In summary, while both tasks involve preparing text data, code generation requires more specialized data cleaning, tokenization, and data augmentation techniques to handle the unique characteristics of code.