论文标题
零击分类方法用于猜测挑战
A Zero-Shot Classification Approach for a Word-Guessing Challenge
论文作者
论文摘要
禁忌挑战赛是一项基于著名禁忌游戏的任务，已提出刺激AI领域的研究。挑战需要建筑系统能够理解猜测者和描述剂的交换消息之间的隐含推断。描述者向间接描述城市的猜测发送了预定的提示，并且需要猜测者返回提示所隐含的匹配城市。爬上评分分类帐需要在指定的时间范围内解决最高数量的提示的最高城市。在这里，我们提出了Taboolm，这是一种语言模型方法，该方法基于零拍设置来应对挑战。我们首先将这种方法的结果与文献研究进行比较。结果表明，我们的方法在禁忌挑战中实现了SOTA的结果，这表明Taboolm可以比现有方法更快，更准确地猜测隐含的城市。
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.
