AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (2110.01691v3)

Abstract: Although LLMs have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, where the output of one step becomes the input for the next, thus aggregating the gains per step. We first define a set of LLM primitive operations useful for Chain construction, then present an interactive system where users can modify these Chains, along with their intermediate results, in a modular way. In a 20-person user study, we found that Chaining not only improved the quality of task outcomes, but also significantly enhanced system transparency, controllability, and sense of collaboration. Additionally, we saw that users developed new ways of interacting with LLMs through Chains: they leveraged sub-tasks to calibrate model expectations, compared and contrasted alternative strategies by observing parallel downstream effects, and debugged unexpected model outputs by "unit-testing" sub-components of a Chain. In two case studies, we further explore how LLM Chains may be used in future applications

• The paper introduces the chaining method for LLM prompts, enhancing transparency and control with a significant 82% improvement in task results.
• The study decomposes complex tasks into primitive operations, enabling stepwise intervention at both local and global levels.
• The approach paves the way for more interpretable, user-centered AI systems and rapid prototyping of explainable human-AI interactions.

Overview of AI Chains: Transparent and Controllable Human-AI Interaction by Chaining LLM Prompts

The paper explores an innovative approach in the domain of interactive AI systems, focusing on increasing transparency and controllability in human-AI interactions by using LLMs. Despite the potential of LLMs to execute various tasks, their scope and unpredictability can hinder performance, particularly for complex tasks. Therefore, the paper introduces the concept of "Chaining" LLM prompts, aimed at enhancing task outcomes and user experience.

The authors define Chaining as the process where the output from one LLM prompt becomes the input for the next. This caters not only to improved task accomplishment but also to increased transparency and control over the LLM systems. Fundamental to this approach is the classification of tasks into a set of primitive LLM operations, such as Classification, Ideation, and Rewriting, each conducive to single LLM runs. The LLM Chains are designed to reflect these operations through distinct steps, fostering user intervention at multiple levels: locally (e.g., editing intermediate data), and globally (e.g., modifying Chain structures).

Two paper cases and a user paper were conducted to validate the notion of Chaining. Results demonstrated that Chaining improved not only the quality of task outcomes but also significantly uplifted user satisfaction metrics concerning transparency, controllability, and collaboration. Notably, users achieved better task results roughly 82% of the time when using the Chaining interface versus a non-Chaining set-up.

The practical implications of Chaining frameworks are substantial. By enabling more explicit model outputs through step-wise tasks, this method allows for a more transparent and interpretable machine learning system, crucial for developing user trust and effective collaboration in AI systems. The authors suggest that the increased transparency and controllability provided by the Chaining method could also serve as a prototype for AI system design, pointing towards advances in the rapid prototyping of AI applications.

Theoretically, this approach introduces a novel way of decomposing AI interactions into smaller, manageable units, potentially guiding future research in adaptive AI systems. The flexibility of this method allows it to accommodate various applications efficiently, highlighting an essential step towards explainable AI systems. Moreover, the simplification of complex tasks into discrete steps can aid in developing more straightforward, user-centered AI systems.

Future advancements might explore further expanding the range of primitive operations and exploring their application in different contexts. Additionally, integrating human-computation steps in the chaining process could also improve real-world applicability and efficiency.

This paper signals a significant move towards practical and theoretically enriched human-AI interaction frameworks, emphasizing the significant role of design and user-centric approaches in AI advancements.