Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910743688904704 |
|---|---|
| author | Rikters, Matīss Reinsone, Sanita |
| author_facet | Rikters, Matīss Reinsone, Sanita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games Rikters, Matīss Reinsone, Sanita Computation and Language Computers and Society 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. |
| title | Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2409.11112 |