The article explores the interplay between structured prompts and Code-LLMs like CODEX and DAVINCI for enhancing task performance. Key findings indicate that both structured inputs and specialized language models contribute significantly, but their combination yields superior effects. The research involved evaluating dynamic prompts across different tasks, noting how training data quality and structure impacted outcomes. Factors like Python formatting were examined, identifying that while certain designs achieve task improvements, no universal structure excels in all scenarios. The results emphasize the complexity of effective model training in real-world applications.
The analysis demonstrates that both the use of Code-LLMs and structured input significantly enhance performance, with their combination yielding greater results than individual approaches.
Dynamic prompts' efficacy is heavily influenced by both the underlying training data and the specific tasks, indicating the importance of context in model performance.
Our extensive study on Python formatting reveals that while some structures can improve task performance, there is no universally effective design applicable across all tasks.
The age-prediction sub-task results illustrate how inter-event edge connections in similar scripts provide beneficial insights, confirming their importance in model training.
#artificial-intelligence #language-models #machine-learning #python-programming #commonsense-reasoning
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