问答基准测试集是实验编程（code-2-art）社群开发的独特数据集，旨在评估人工智能模型在实际应用场景中的问答能力。与传统基准测试不同，这些数据集强调实用性和创造性，而非标准化评估。
测试使用的问题
答题基本信息，很文件夹中相应文件
- 真实需求驱动: 由社群成员根据实际使用场景和需求整理
- 开放评估: 不设预定标准答案，关注AI回应的实用价值和创造性
- 技术无限制: 允许使用系统提示词等技巧，模拟真实应用环境
- 能力探索: 测试AI在不受限条件下的真实能力边界
- 社群协作: 集体贡献问题和评估反馈，形成进化型测试集
- 提示词工程交流: 分享和比较不同提示策略的效果
- 真实能力评估: 测试模型在实际任务中的表现
- 应用开发参考: 为创新应用提供性能参考
- 社群学习: 集体学习和改进AI使用方法
- 跨界实验: 探索AI在艺术、设计、编程交叉领域的应用
- 问题库: 按应用场景和难度分类的真实问题集
- 答题库: 记录不同模型、不同提示策略下的回答
- 评估记录: 社群成员对回答效果的多维度评价
- 提示词库: 有效提示词的收集和分类
- 创造性: 解决方案的新颖性和独特视角
- 实用性: 解决实际问题的有效程度
- 灵活性: 适应不同表述和需求变化的能力
- 交互体验: 回答的连贯性、清晰度和参与感
- 社群反馈: 实际使用者的主观评价和改进建议
The Q&A Benchmark Dataset is a unique collection developed by the Code-2-Art experimental programming community to evaluate AI models' question-answering capabilities in practical application scenarios. Unlike traditional benchmarks, these datasets emphasize utility and creativity rather than standardized assessment.
- Real-need Driven: Compiled by community members based on actual usage scenarios
- Open Evaluation: No predetermined standard answers, focusing on practical value and creativity
- Technique Unrestricted: Allows system prompts and other techniques, simulating real application environments
- Capability Exploration: Tests AI's true capabilities under unrestricted conditions
- Community Collaboration: Collective contribution of questions and assessment feedback, forming an evolving test set
- Prompt Engineering Exchange: Sharing and comparing different prompting strategies
- Real Capability Assessment: Testing model performance in actual tasks
- Application Development Reference: Providing performance references for innovative applications
- Community Learning: Collective learning and improvement of AI usage methods
- Interdisciplinary Experiments: Exploring AI applications in art, design, and programming intersections
- Question Repository: Real-world questions categorized by application scenarios and difficulty
- Answer Collection: Responses recorded under different models and prompting strategies
- Evaluation Records: Multi-dimensional assessments from community members
- Prompt Library: Collection and classification of effective prompts
- Creativity: Novelty of solutions and unique perspectives
- Practicality: Effectiveness in solving real problems
- Flexibility: Ability to adapt to different phrasings and changing requirements
- Interaction Experience: Coherence, clarity, and engagement of responses
- Community Feedback: Subjective evaluations and improvement suggestions from actual users
这个社群驱动的测试集代表了一种新型的AI评估方法，它不仅关注模型的技术能力，更注重实际应用价值和创新可能性，为AI在实验编程与艺术创作领域的发展提供了独特视角。
This community-driven test set represents a new approach to AI evaluation that focuses not just on technical capabilities but on practical application value and innovative possibilities, providing a unique perspective for AI development in experimental programming and artistic creation.