A Framework for Lightweight Responsible Prompting Recommendation
This work addresses the problem of insufficient best practices for end-users interacting with generative AI, though it appears incremental as it builds on existing methods like sentence transformers and similarity metrics.
The authors tackled the lack of guidance for user interactions with generative AI by proposing a lightweight framework for responsible prompting recommendations, which includes a human-curated dataset, similarity metrics, and an evaluation step to help users add positive values and remove harmful content.
Computer Science and Design practitioners have been researching and proposing alternatives for a dearth of recommendations, standards, or best practices in user interfaces for decades. Now, with the advent of generative Artificial Intelligence (GenAI), we have yet again an emerging, powerful technology that lacks sufficient guidance in terms of possible interactions, inputs, and outcomes. In this context, this work proposes a lightweight framework for responsible prompting recommendation to be added before the prompt is sent to GenAI. The framework is comprised of (1) a human-curated dataset for recommendations, (2) a red team dataset for assessing recommendations, (3) a sentence transformer for semantics mapping, (4) a similarity metric to map input prompt to recommendations, (5) a set of similarity thresholds, (6) quantized sentence embeddings, (7) a recommendation engine, and (8) an evaluation step to use the red team dataset. With the proposed framework and open-source system, the contributions presented can be applied in multiple contexts where end-users can benefit from guidance for interacting with GenAI in a more responsible way, recommending positive values to be added and harmful sentences to be removed.