modeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models

• URL: http://arxiv.org/abs/2406.17038v1
• Date: Mon, 24 Jun 2024 18:00:59 GMT
• Title: modeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models
• Authors: Nathan A. Chi, Teodor Malchev, Riley Kong, Ryan A. Chi, Lucas Huang, Ethan A. Chi, R. Thomas McCoy, Dragomir Radev,
• Abstract summary: modeLing is a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. We evaluate several large open source language models and GPT on our benchmark.
• Score: 23.105555180223487
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce modeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of a language's grammatical structure from a small number of examples. Such puzzles provide a natural testbed for language models, as they require compositional generalization and few-shot inductive reasoning. Consisting solely of new puzzles written specifically for this work, modeLing has no risk of appearing in the training data of existing AI systems: this ameliorates the risk of data leakage, a potential confounder for many prior evaluations of reasoning. Evaluating several large open source language models and GPT on our benchmark, we observe non-negligible accuracy, demonstrating few-shot emergent reasoning ability which cannot merely be attributed to shallow memorization. However, imperfect model performance suggests that modeLing can be used to measure further progress in linguistic reasoning.

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