论文标题
通过将语言模型与符号推理引擎相结合来改善国际象棋评论
Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines
论文作者
论文摘要
尽管最近在语言建模方面取得了许多进步，但最先进的语言模型在现实世界中缺乏基础，并且在涉及复杂推理的任务中挣扎。同时，AI的象征性推理能力的进步导致了在国际象棋和GO等游戏中的表现（Silver等，2018）的系统。国际象棋评论提供了一个有趣的领域，用于弥合这两个研究领域，因为它需要在复杂的董事会状态下进行推理并提供自然语言的分析。在这项工作中，我们演示了如何将符号推理引擎与可控语言模型相结合以产生国际象棋评论。我们进行实验以证明我们的方法会产生人类法官比以前基线的评论。
Despite many recent advancements in language modeling, state-of-the-art language models lack grounding in the real world and struggle with tasks involving complex reasoning. Meanwhile, advances in the symbolic reasoning capabilities of AI have led to systems that outperform humans in games like chess and Go (Silver et al., 2018). Chess commentary provides an interesting domain for bridging these two fields of research, as it requires reasoning over a complex board state and providing analyses in natural language. In this work we demonstrate how to combine symbolic reasoning engines with controllable language models to generate chess commentaries. We conduct experiments to demonstrate that our approach generates commentaries that are preferred by human judges over previous baselines.
