This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT-enabling it to generate Science Birds levels with high stability and character-like qualities-fully using their creativity as well as prompt engineering skills. ChatGPT is a conversational agent developed by OpenAI. Science Birds is selected as the competition platform because designing an Angry Birds-like level is not a trivial task due to the in-game gravity; the playability of the levels is determined by their stability. To lower the entry barrier to the competition, we limit the task to the generation of capitalized English alphabetical characters. Here, the quality of the generated levels is determined by their stability and similarity to the given characters. A sample prompt is provided to participants for their reference. An experiment is conducted to determine the effectiveness of its modified versions on level stability and similarity by testing them on several characters. To the best of our knowledge, we believe that ChatGPT4PCG is the first competition of its kind and hope to inspire enthusiasm for prompt engineering in procedural content generation.

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The Krathu-500 contains 574 Pantip posts title, post body with all comments of each post. The number of total comments is at 63,293 comments. The corpus provide Thai language used in real life situation with various context and types in conversational form. The corpus serves as a good way to improve capability of machine learning techniques that dealing with Thai language. Sentiment labeled smaller version of the comments dataset also provided with 6,306 records. The labeled corpus is human-annotated dataset with three labels for negative, neutral, and positive comments. The project also consists of open-source repository that allow any people who interested to modify and built on top of the current source code and dataset.

The Krathu-500 contains 574 Pantip posts title, post body with all comments of each post. The number of total comments is at 63,293 comments. The corpus provide Thai language used in real life situation with various context and types in conversational form. The corpus serves as a good way to improve capability of machine learning techniques that dealing with Thai language. Sentiment labeled smaller version of the comments dataset also provided with 6,306 records. The labeled corpus is human-annotated dataset with three labels for negative, neutral, and positive comments. The project also consists of open-source repository that allow any people who interested to modify and built on top of the current source code and dataset.

The Krathu-500 contains 574 Pantip posts title, post body with all comments of each post. The number of total comments is at 63,293 comments. The corpus provide Thai language used in real life situation with various context and types in conversational form. The corpus serves as a good way to improve capability of machine learning techniques that dealing with Thai language. Sentiment labeled smaller version of the comments dataset also provided with 6,306 records. The labeled corpus is human-annotated dataset with three labels for negative, neutral, and positive comments. The project also consists of open-source repository that allow any people who interested to modify and built on top of the current source code and dataset.