Challenge: Sentence simplification (SS) aims to make sentences more straightforward to read and understand without changing its key points.
Approach: They compare 26 state-of-the-art LLMs in Portuguese SS with two simplification models trained explicitly for this task and language.
Outcome: The proposed models outperform open-source models in Portuguese SS . the models are compared against two simplification models trained for Portuguese .

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Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features (2024.findings-acl)

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Challenge: Automated text simplification requires (paired) datasets that are scarce in languages other than English.
Approach: They propose a method that leverages paraphrases, context, and linguistic attributes to overcome the absence of paired texts in Portuguese.
Outcome: The proposed model surpasses the current state-of-the-art while competing with a Large Language Model.
BLESS: Benchmarking Large Language Models on Sentence Simplification (2023.emnlp-main)

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Challenge: BLESS is a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS).
Approach: They present a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS).
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Text Simplification from Professionally Produced Corpora (L18-1)

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Challenge: Existing approaches to Text Simplification rely on the Wikipedia-Simple Wikipedia parallel corpus, which is used for many tasks.
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Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches (2020.coling-main)

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Challenge: Sentence complexity assessment is a relatively new task in Natural Language Processing.
Approach: They propose to use Brazilian Portuguese to evaluate sentences with linguistic features to improve readability.
Outcome: The proposed model reaches the state-of-the-art for Brazilian Portuguese with 97.8% accuracy with linguistic features.
A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese (C18-1)

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Challenge: Effective textual communication depends on readers being proficient enough to comprehend texts . when meaning is not well conveyed, many losses and damages may occur .
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ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification (2022.coling-1)

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Challenge: Lexical simplification (LS) is the task of replacing complex words with simpler alternatives to make texts more accessible to various target populations.
Approach: They propose to use a Brazilian Portuguese multi-candidate dataset to test LS systems.
Outcome: The proposed model outperforms existing models on Brazilian Portuguese and Brazilian newspaper articles.
Let’s Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification (2026.findings-eacl)

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Challenge: Large language models demonstrate limited capability in proficiency-controlled sentence simplification when simplifying across large readability levels.
Approach: They propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning.
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Replace, Paraphrase or Fine-tune? Evaluating Automatic Simplification for Medical Texts in Spanish (2024.lrec-main)

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Challenge: lexicon-based simplification methods can help patients understand medical documents . but they must ensure that the content is transmitted rigorously and not creating wrong information.
Approach: They tested automatic simplification techniques using a Spanish lexicon of technical and laymen terms.
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Comparing human and language models sentence processing difficulties on complex structures (2026.acl-long)

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Challenge: Large language models (LLMs) that converse with humans are a reality, but do LLMs experience human-like processing difficulties?
Approach: They systematically compare human and LLM sentence comprehension across seven challenging linguistic structures.
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Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)

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Challenge: Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating.
Approach: They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages.
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