Papers by Lukas Edman
CUTE: Measuring LLMs’ Understanding of Their Tokens (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on a wide variety of tasks, authors say . they lack direct access to characters, which can be difficult to generalize to new languages . |
| Approach: | They propose a benchmark to test the orthographic knowledge of Large Language Models . they find that most LLMs seem to know the spelling of their tokens - yet fail to manipulate text . |
| Outcome: | The proposed benchmark tests the orthographic knowledge of large language models . it finds that most LLMs seem to know the spelling of their tokens, but fail to manipulate text . |
EXECUTE: A Multilingual Benchmark for LLM Token Understanding (2025.findings-acl)
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| Challenge: | EXECUTE is an expandable X(Cross)-Lingual Extension of CUTE that can be expanded to any language. |
| Approach: | They extend the CUTE benchmark to more languages with diverse scripts and writing systems, introducing EXECUTE. |
| Outcome: | The extended framework allows expansion to any language. |
Positional Overload: Positional Debiasing and Context Window Extension for Large Language Models using Set Encoding (2025.acl-long)
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| Challenge: | Large Language Models typically track the order of tokens using positional encoding, which causes two significant limitations: 1. Positional Bias: When processing long text sequences, the number of token can exceed the range the model was trained on. |
| Approach: | They propose a method that allows multiple pieces of text to be encoded in the same position, eliminating positional bias entirely. |
| Outcome: | The proposed method eliminates positional bias entirely and increases the size of the input an LLM can handle. |
Subword-Delimited Downsampling for Better Character-Level Translation (2022.findings-emnlp)
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| Challenge: | Subword-level models are expensive in terms of time and computation, but character-level model with downsampling component can be used for machine translation. |
| Approach: | They propose a character-level downsampling method which is informed by subwords to improve model performance. |
| Outcome: | The proposed method outperforms existing methods and shows that it can be done without sacrificing quality. |