Word Synchronization Challenge: A Benchmark for Word Association Responses for Large Language Models

TL;DR

This paper presents the Word Synchronization Challenge, a new benchmark for evaluating large language models' ability to mimic human word association responses in interactive settings, advancing HCI research.

Contribution

It introduces a dynamic, game-like benchmark to assess LLMs' human-like cognitive and conversational abilities in HCI contexts.

Findings

Model sophistication influences performance

Insights into models' ability to engage in social interactions

Potential for more empathetic human-machine collaboration

Abstract

This paper introduces the Word Synchronization Challenge, a novel benchmark to evaluate large language models (LLMs) in Human-Computer Interaction (HCI). This benchmark uses a dynamic game-like framework to test LLMs ability to mimic human cognitive processes through word associations. By simulating complex human interactions, it assesses how LLMs interpret and align with human thought patterns during conversational exchanges, which are essential for effective social partnerships in HCI. Initial findings highlight the influence of model sophistication on performance, offering insights into the models capabilities to engage in meaningful social interactions and adapt behaviors in human-like ways. This research advances the understanding of LLMs potential to replicate or diverge from human cognitive functions, paving the way for more nuanced and empathetic human-machine collaborations.