Word Synchronization Challenge: A Benchmark for Word Association Responses for LLMs
This work addresses the need for better benchmarks to assess LLMs' ability to engage in meaningful social interactions for improved human-machine collaborations, though it appears incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of evaluating large language models (LLMs) in mimicking human cognitive processes for human-computer interaction by introducing the Word Synchronization Challenge benchmark, with initial findings showing that model sophistication influences performance in word association tasks.
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.