Beyond the Black Box: Demystifying Multi-Turn LLM Reasoning with VISTA

TL;DR

VISTA is an interactive visualization tool designed to analyze and understand multi-turn reasoning processes in large language models, making it easier to interpret complex decision pathways and compare different models.

Contribution

The paper introduces VISTA, a novel web-based system that visualizes reasoning dependencies and enables interactive analysis of multi-turn LLM interactions.

Findings

Reduces cognitive load in analyzing LLM reasoning

Enables 'what-if' scenario testing for model decisions

Supports integration of custom benchmarks and models

Abstract

Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the intricate reasoning processes within these interactions presents a significant challenge due to complex contextual dependencies and a lack of specialized visualization tools, leading to a high cognitive load for researchers. To address this gap, we present VISTA, an web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks. VISTA allows users to visualize the influence of context on model decisions and interactively modify conversation histories to conduct "what-if" analyses across different models. Furthermore, the platform can automatically parse a session and generate a reasoning dependency tree, offering a transparent view of the model's step-by-step logical path. By providing a unified and interactive framework, VISTA significantly reduces the complexity of analyzing reasoning chains, thereby facilitating a deeper understanding of the capabilities and limitations of current LLMs. The platform is open-source and supports easy integration of custom benchmarks and local models.