Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses. Yet tools that go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt engineering and on-demand hypothesis testing of text generation LLMs. ChainForge provides a graphical interface for comparison of responses across models and prompt variations. Our system was designed to support three tasks: model selection, prompt template design, and hypothesis testing (e.g., auditing). We released ChainForge early in its development and iterated on its design with academics and online users. Through in-lab and interview studies, we find that a range of people could use ChainForge to investigate hypotheses that matter to them, including in real-world settings. We identify three modes of prompt engineering and LLM hypothesis testing: opportunistic exploration, limited evaluation, and iterative refinement.

翻译：评估大语言模型（LLM）的输出具有挑战性，需要生成并理解大量响应。然而，现有超越基础提示功能的工具通常要求掌握编程API知识、局限于特定领域或为闭源系统。我们提出ChainForge，一个面向文本生成LLM的提示工程与按需假设检验的开源可视化工具包。ChainForge提供图形界面，支持跨模型和提示变体的响应比较。该系统旨在支持三项任务：模型选择、提示模板设计和假设检验（如审计）。我们在开发早期阶段即发布ChainForge，并与学术界及在线用户迭代优化其设计。通过实验室研究和访谈研究，我们发现不同背景的用户可使用ChainForge探究其关注的假设，包括真实场景中的应用。我们识别出提示工程与LLM假设检验的三种模式：机会性探索、有限评估与迭代优化。