A Zero-Shot Classification Approach for a Word-Guessing Challenge

TL;DR

This paper introduces TabooLM, a zero-shot language model approach that achieves state-of-the-art results in the Taboo Challenge by accurately and efficiently inferring implied cities from indirect hints.

Contribution

The paper presents a novel zero-shot language model method, TabooLM, that outperforms existing approaches in the Taboo Challenge for city inference tasks.

Findings

TabooLM achieves state-of-the-art performance on the Taboo Challenge.

TabooLM guesses implied cities faster than previous methods.

TabooLM demonstrates higher accuracy in city inference from indirect hints.

Abstract

The Taboo Challenge competition, a task based on the well-known Taboo game, has been proposed to stimulate research in the AI field. The challenge requires building systems able to comprehend the implied inferences between the exchanged messages of guesser and describer agents. A describer sends pre-determined hints to guessers indirectly describing cities, and guessers are required to return the matching cities implied by the hints. Climbing up the scoring ledger requires the resolving of the highest amount of cities with the smallest amount of hints in a specified time frame. Here, we present TabooLM, a language-model approach that tackles the challenge based on a zero-shot setting. We start by presenting and comparing the results of this approach with three studies from the literature. The results show that our method achieves SOTA results on the Taboo challenge, suggesting that TabooLM can guess the implied cities faster and more accurately than existing approaches.