2305.08360.pdf- “Improving ChatGPT’s Code Generation Performance: Prompt Design Strategies” – “Evaluating ChatGPT on CodeXGlue Dataset for Code Generation Tasks” – “Optimizing ChatGPT’s Code Generation with Prompt Engineering” – “Replication Package for Future Research in ChatGPT’s Code Generation” – “Comparing CodeBERT and CodeGPT: Language Models for Code Generation”

– Paper evaluates ChatGPT’s code generation capabilities using CodeXGlue dataset.
– Prompts are designed and optimized to guide ChatGPT for better code generation.
– Experimental results show improved performance with specific prompt requirements.
– ChatGPT’s generation randomness has little effect on performance.
– Performance compared with state-of-the-art fine-tuned LLMs and code quality analyzed.

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– The paper mentions OpenAI’s language model ChatGPT as a powerful tool for generating human-like responses.
– The effectiveness of ChatGPT for code generation is evaluated in the paper.
– The paper compares the performance of the best prompts with state-of-the-art finetuned LLMs.

– Designed and improved prompts for guiding ChatGPT in code generation tasks.
– Demonstrated the effectiveness of prompts in generating code on CodeXGlue dataset.
– Investigated influential factors for prompt design in code generation tasks.
– Compared prompt performance with state-of-the-art finetuned LLMs.
– Assessed correctness and quality of code generated by ChatGPT.
– Presented potential future research directions in code generation.

– Evaluating ChatGPT on CodeXGlue dataset for text-to-code and code-to-code generation tasks.
– Proposing prompt design and optimization methods to improve code generation.
– Analyzing the effectiveness of prompt design and the impact of conciseness request.

– Designed and improved prompts for guiding ChatGPT in code generation tasks.
– Prompts were effective in generating code on the CodeXGlue dataset.
– Investigated influential factors for prompt design in code generation tasks.
– Compared prompt performance with state-of-the-art finetuned LLMs.
– Assessed correctness and quality of code generated by ChatGPT.
– Presented potential future research directions.

– Experimental settings and results for four research questions were presented.

– The researchers tested a language model called ChatGPT for generating code.
– They used a dataset called CodeXGlue to evaluate ChatGPT’s performance.
– They found that by designing prompts carefully, ChatGPT can generate better code.
– They also provided insights on how to improve prompt design for future research.