Challenge: Existing studies on linguistic efficiency have focused on the systematicity of forms, a key property of natural language.
Approach: They propose to incorporate regularity across sets of forms in studies of efficiency in language . they use the Minimum Description Length approach to measure regularity and processing complexity .
Outcome: The proposed method shows that recursive numeral systems are more efficient with respect to regularity and processing complexity.

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Systematicity between Forms and Meanings across Languages Supports Efficient Communication (2026.acl-long)

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Challenge: Languages vary in how meanings map to word forms, but this theory does not account for systematic relations within word forms.
Approach: They propose a model that measures the learnability of meaning-to-form mappings by inverse of simplicity.
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High Performance Natural Language Processing (2020.emnlp-tutorials)

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Challenge: a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field .
Approach: This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective.
Outcome: This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective.
On Efficiently Representing Regular Languages as RNNs (2024.findings-acl)

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Challenge: Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs).
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NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)

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Challenge: Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations.
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Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles (2025.emnlp-main)

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Challenge: Across languages, numeral systems vary widely in how they construct and combine numbers.
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Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning (2024.acl-long)

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Challenge: Existing methods to solve compositional tasks are limited by complexity and complexity.
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Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)

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Challenge: Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows.
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Incremental Natural Language Processing: Challenges, Strategies, and Evaluation (C18-1)

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Challenge: In this survey, I consolidate and categorize the approaches, identifying similarities and differences in computation and data, and show trade-offs that have to be considered.
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Frequency & Compositionality in Emergent Communication (2025.emnlp-main)

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Challenge: Natural languages exhibit a universal tendency to resist regular patterns, developing idiosyncratic forms.
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Representing Numbers in NLP: a Survey and a Vision (2021.naacl-main)

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Challenge: Numeracy is an essential skill for language understanding since numbers are often interspersed in text.
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