Computer Science > Computation and Language
[Submitted on 26 Feb 2021 (v1), last revised 13 May 2022 (this version, v2)]
Title:Chess as a Testbed for Language Model State Tracking
View PDFAbstract:Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world state underlying the text. Motivated by this issue, we consider the task of language modeling for the game of chess. Unlike natural language, chess notations describe a simple, constrained, and deterministic domain. Moreover, we observe that the appropriate choice of chess notation allows for directly probing the world state, without requiring any additional probing-related machinery. We find that: (a) With enough training data, transformer language models can learn to track pieces and predict legal moves with high accuracy when trained solely on move sequences. (b) For small training sets providing access to board state information during training can yield significant improvements. (c) The success of transformer language models is dependent on access to the entire game history i.e. "full attention". Approximating this full attention results in a significant performance drop. We propose this testbed as a benchmark for future work on the development and analysis of transformer language models.
Submission history
From: Shubham Toshniwal [view email][v1] Fri, 26 Feb 2021 01:16:23 UTC (1,023 KB)
[v2] Fri, 13 May 2022 21:40:30 UTC (613 KB)
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