Both methods utilize the model’s zero-shot outputs as demonstrations for prompting itself, selecting reliable self-generated demonstrations based on confident and consistent model predictions. The methods were evaluated across different benchmarks, with results showing that both COSP and USP outperform standard zero-shot baselines and remain competitive when compared to prompting with golden examples. The research reinforces the efficacy of these methods in enhancing the performance of language models across diverse natural language understanding and generation tasks.
Key takeaways:
- Google Research has introduced two new methods, Consistency-Based Self-Adaptive Prompting (COSP) and Universal Self-Adaptive Prompting (USP), to enhance common sense reasoning capabilities in large language models (LLMs).
- COSP and USP utilize the model’s zero-shot outputs as demonstrations for prompting itself, selecting reliable self-generated demonstrations based on confident and consistent model predictions.
- USP extends the approach to a wider array of natural language processing tasks, including classification, short-form generation, and long-form generation, adapting the confidence measurement techniques accordingly.
- Both COSP and USP have shown to outperform baseline methods in various tasks and benchmarks, demonstrating their effectiveness in enhancing the performance of language models across diverse natural language understanding and generation tasks.