A Deep User Interface for Exploring LLaMa

TL;DR

This paper introduces a visual analytics tool designed to help users explore and compare large language models like LLaMa by adjusting hyperparameters and visualizing outputs, improving user understanding and interaction.

Contribution

The paper presents a novel interactive visual analytics tool with hyperparameter controls for exploring LLM outputs, supported by user study insights.

Findings

User feedback favored the visual design and interface layout.

The tool facilitated better understanding of LLM behaviors.

Insights gained can improve future Human-LLM interaction tools.

Abstract

The growing popularity and widespread adoption of large language models (LLMs) necessitates the development of tools that enhance the effectiveness of user interactions with these models. Understanding the structures and functions of these models poses a significant challenge for users. Visual analytics-driven tools enables users to explore and compare, facilitating better decision-making. This paper presents a visual analytics-driven tool equipped with interactive controls for key hyperparameters, including top-p, frequency and presence penalty, enabling users to explore, examine and compare the outputs of LLMs. In a user study, we assessed the tool's effectiveness, which received favorable feedback for its visual design, with particular commendation for the interface layout and ease of navigation. Additionally, the feedback provided valuable insights for enhancing the effectiveness of Human-LLM interaction tools.