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Training AI models is challenging, particularly when crafting behavior instructions.
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Prompting for Discovery: Flexible Sense-Making for AI Art-Making with Dreamsheets
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ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing
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Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23) . Association for Computing Machinery, New York, NY, USA, Article 96, 14 pages
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PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23) . Association for Computing Machinery, New York, NY, USA, Article 6, 17 pages
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Promptbreeder: Self-referential self-improvement via prompt evolution
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
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Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23) . Association for Computing Machinery, New York, NY, USA, 3953–3957
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Graphologue: Exploring Large Language Model Responses with Interactive Diagrams. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23) . Association for Computing Machinery, New York, NY, USA, Article 3, 20 pages
Star-gate: Teaching language models to ask clarifying questions
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Dices dataset: Diversity in conversational ai evaluation for safety
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Prompt Guide
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Graph of thoughts: Solving elaborate problems with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 17682–17690
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PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models
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Peiling Jiang, Jude Rayan, Steven P. Dow, and Haijun Xia. 2023 · 2023
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Dspy: Compiling declarative language model calls into self-improving pipelines
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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RogerEbert.com
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Supporting Sensemaking of Large Language Model Outputs at Scale
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Clarify: Improving model robustness with natural language corrections. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology . 1–19
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OpenAI Assistant
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Best practices for prompt engineering with the OpenAI API
OpenAI. 2024 · 2024
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The DALL·E 2 Prompt Book
Guy Parsons. 2022 · 2024
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Improving Context-Aware Preference Modeling for Language Models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems
Silviu Pitis, Ziang Xiao, Nicolas Le Roux, and Alessandro Sordoni. 2024 · 2024
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Show, Don’t Tell: Aligning Language Models with Demonstrated Feedback
Omar Shaikh, Michelle Lam, Joey Hejna, Yijia Shao, Michael Bernstein, and Diyi Yang. 2024 · 2024
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SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines
Shreya Shankar, Haotian Li, Parth Asawa, Madelon Hulsebos, Yiming Lin, J. D. Zamfirescu-Pereira, Harrison Chase, Will Fu-Hinthorn, Aditya G. Parameswaran, and Eugene Wu. 2024 · 2024
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Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation. In Proceedings of the CHI Conference on Human Factors in Computing Systems (<conf-loc>, <city>Honolulu</city>, <state>HI</state>, <country>USA</country>, </conf-loc>) (CHI ’24) . Association for Computing Machinery, New York, NY, USA, Article 644, 26 pages
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Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
Zijie J Wang, Chinmay Kulkarni, Lauren Wilcox, Michael Terry, and Michael Madaio. 2024 · 2024
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