Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games

Autor:	Rikters, Matīss, Reinsone, Sanita
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	At the beginning of 2022, a simplistic word-guessing game took the world by storm and was further adapted to many languages beyond the original English version. In this paper, we examine the strategies of daily word-guessing game players that have evolved during a period of over two years. A survey gathered from 25% of frequent players reveals their strategies and motivations for continuing the daily journey. We also explore the capability of several popular open-access large language model systems and open-source models at comprehending and playing the game in two different languages. Results highlight the struggles of certain models to maintain correct guess length and generate repetitions, as well as hallucinations of non-existent words and inflections. Comment: Published in the 4th Wordplay: When Language Meets Games Workshop @ ACL 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.11112 Zobrazit plný text záznamu View this record from Arxiv