ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing

WorkDifferentWithAI.com Academic Paper Alert!

Written by Ian Arawjo, Chelse Swoopes, Priyan Vaithilingam, Martin Wattenberg, Elena Glassman

Category: “Enterprise AI”

Article Section: Natural language processing

Publication Date: 2023-09-16

SEO Description: “ChainForge: Visual Toolkit, LLM Hypothesis Testing, AI, Human-Computer Interaction, Text Generation”

Claude-Generated Paper Summary

Here is an analysis of the key points from this paper:

Novelty:

• The paper presents ChainForge, a new open-source visual toolkit for prompt engineering and testing hypotheses about large language model (LLM) behavior. This fills a gap between playground-style tools for exploration and programming-based tools for systematic testing.
• ChainForge allows combintorial querying of multiple models with multiple prompts and input parameters all at once. This combinatorial power is novel and enables more robust testing and comparison of LLM outputs.
• The paper identifies three key modes of usage for prompt engineering and LLM hypothesis testing: opportunistic exploration, limited evaluation, and iterative refinement. This provides a useful framework for understanding practices in this emerging area.
• The qualitative user studies provide new insights into how real users apply prompt engineering tools, especially framing prompt engineering as a subtask of prototyping data processing pipelines. This points to the need for better support in transitioning prompts from prototypes to production.

Commercial applications:

• ChainForge could be productized as a standalone commercial software tool for prompt engineering and LLM testing. There is demand for such tools from ML developers, researchers, and auditors.
• The concepts from ChainForge could be integrated into commercial LLM services like OpenAI or Anthropic to improve their developer/prompting interfaces.
• Consulting firms and agencies could use ChainForge for more systematic testing and comparison when developing LLM-based solutions for clients.
• The modes of usage identified could inform design of commercial prompt engineering services and LLM infrastructure.
• OpenAI or others may be interested in acquiring or licensing ChainForge technology/IP. The open-source nature facilitates commercialization.

In summary, the novel interface concepts and qualitative insights make this a valuable contribution both to HCI research and for potential commercialization opportunities around improving LLM prompting and testing tools.

Keywords

ChainForge, Visual Toolkit, Prompt Engineering, LLM Hypothesis Testing, Large Language Models

Author’s Abstract

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.

Read the full paper here