Go to ACL ARR 2024 April homepageCUTE: Measuring LLMs' Understanding of Their Tokens
Abstract: Large Language Models (LLMs) show remarkable performance on a wide variety of tasks. Most LLMs split text into multi-character tokens and process them as atomic units without direct access to individual characters. This raises the question: To what extent can LLMs learn orthographic information? To answer this, we propose a new benchmark, CUTE, which features a collection of tasks designed to test the orthographic knowledge of LLMs. We evaluate popular LLMs on CUTE, finding that most of them seem to know the spelling of their tokens, yet fail to use this information effectively to manipulate text, calling into question how much of this knowledge is generalizable.
Paper Type: Short
Research Area: Language Modeling
Research Area Keywords: benchmarking, robustness, prompting, evaluation, subword representations
Languages Studied: English
Submission Number: 328
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