Semantic Navigation for AI-assisted Ideation

TL;DR

It is found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration.

Abstract

We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations. We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration. We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.

Figures (10)

• Figure 1: The pair of LLM-based mappings between the spaces of problem statements and solution statements.
• Figure 2: Exploring the Problem Statement space by finding related known problem statements and generating a new problem statement using the two LLM + LoRA mappings of Fig. \ref{['fig:llmprobsoln']}.
• Figure 3: Illustrating a wide depth-first exploration of the Problem Statement space by iteratively applying the two LLM + LoRA mappings of Fig. \ref{['fig:llmprobsoln']} to the newest generated problem statement, or alternatively, by searching for known problem statements related to one of the known problem statements discovered in the previous iteration.
• Figure 4: Evaluation of input filtering conditions.
• Figure 5: Slack Ideation Assistant UI.