mlsanityData to 14 Oct 2026

A Framework for Lightweight Responsible Prompting Recommendation

arXiv 2504.08757 ↗First posted 29 Mar 2025cs.IR cs.AIcited by 0

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 13 of its 48 references that are in the map. First posted on arXiv 29 Mar 2025.

Builds on

13 of 48 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  6. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  7. 2020Artificial Intelligence, Values and Alignmentcited by 448
  8. 2023Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discoverycited by 336
  9. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  10. 2017Adaptive Quantization for Deep Neural Networkcited by 67
  11. 2023What's the Magic Word? A Control Theory of LLM Promptingcited by 41
  12. 2019Correlation Coefficients and Semantic Textual Similaritycited by 20
1 more
  1. 2023Compressing Sentence Representation with maximum Coding Rate Reductioncited by 1

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  1. 2025Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
  2. 2025Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
  3. 2024Facilitating Human Feedback for GenAI Prompt Optimization
  4. 2025Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
  5. 2026GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
  6. 2026Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system
  7. 2026Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management
  8. 2025Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations ↗