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Main Authors: Edman, Lukas, Schmid, Helmut, Fraser, Alexander
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.15452
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author Edman, Lukas
Schmid, Helmut
Fraser, Alexander
author_facet Edman, Lukas
Schmid, Helmut
Fraser, Alexander
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_15452
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle CUTE: Measuring LLMs' Understanding of Their Tokens
Edman, Lukas
Schmid, Helmut
Fraser, Alexander
Computation and Language
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.
title CUTE: Measuring LLMs' Understanding of Their Tokens
topic Computation and Language
url https://arxiv.org/abs/2409.15452