Text Readability and Simplification

Últimos artículos

• Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons

  Bohao Wu, Qingyun Wang, Yue Guo · 22 de septiembre de 2026

  Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgrounds. However, tailoring models to individual users typically requires substantial annotation efforts and computational resources due to user-specific fin…

• Assessing Readability with LLMs: The Role of Reasoning and Few-Shot Prompting

  Rapha\"el Thieffry, Matej Martinc · 22 de septiembre de 2026

  Readability assessment is essential for tailoring texts to intended audiences across educational, healthcare, and information retrieval domains. However, traditional readability formulas struggle to generalize across genres and languages, while supervised machine learning models rely on scarce, doma…

• Register Bias in Complexity-Based Large Language Model Routing

  Simran Koul · 17 de septiembre de 2026

  Large language model services increasingly route each query to one of several models of differing capability, using a cheap estimate of query complexity to send easy queries to small models and hard queries to large ones. I show that this routing step is not register neutral: text written in a non-s…

• Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

  Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen · 16 de septiembre de 2026

  This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medical language, bridging a critical gap between the complexity of healthcare texts and patients' reading comprehension. Rece…

• Limits of LLM Text Detectors in Education

  Lukas Gehring, Benjamin Paa{\ss}en · 14 de septiembre de 2026

  Students increasingly use the assistance of large language models (LLMs) in their academic writing. While slight assistance (e.g., grammar and style correction, as well as feedback) is permitted under most institutional policies, it is usually forbidden to offload entire writing tasks to LLMs. Unfor…

• Analyzing Traditional and Neural Approaches to Multilingual Readability Assessment

  Joshua Wong, Chris Tanner · 11 de septiembre de 2026

  Transformer-based models excel at Automatic Readability Assessment (ARA), yet feature-based models remain in active use because their predictions tie back to linguistic properties. This matters because readability labels are subjective and rater-dependent, so high accuracy on noisy ground truth may …

• Reinforcement Learning for improving Large Language Models' Catalan text simplification capabilities

  Arnau Ayguad\'e Domingo, Stefan Bott, Horacio Saggion · 7 de septiembre de 2026

  Although automatic text simplification (ATS) is critical for accessibility, its progress has not matched the rapid evolution of broader natural language processing techniques. This paper investigates the application of reinforcement learning (RL) to improve the quality of ATS for low-resource langua…

• Language Proficiency Assessment from Eye Movements in Naturalistic Passage Reading

  Shachar Frenkel, Ido Falah, Omer Shubi, Yevgeni Berzak · 1 de septiembre de 2026

  Standard language proficiency tests rely on linguistic tasks such as vocabulary, grammar and reading comprehension quizzes. An alternative, cognitively motivated approach, introduced in Berzak et al. (2018), proposed instead to predict language proficiency from behavioral traces of eye movements in …

• SimpCue: Cue-Based Prompting for Multilingual Text Simplification

  Mehrzad Tareh, Horacio Saggion, Stefan Bott · 31 de agosto de 2026

  Text simplification aims to make complex texts easier to understand while preserving their original meaning. Recent large language models can perform simplification through prompting, but it remains unclear whether adding explicit linguistic information about sentence complexity to the prompt improv…

• Flesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Models

  Yo Ehara · 25 de agosto de 2026

  Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL) are widely used readability scores for English computed from the same two document statistics, yet their stability on long documents need not imply invariance to lexical composition. Surprisingly, under a topic model with an explici…

• When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation

  Chenchen Mao, Hanjing Shi, Haiyan Jia, Emily Wegrzyn, Dominic DiFranzo · 20 de agosto de 2026

  Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves. We investigated how source-text condition and output rendering relate to perceived translation quality, and how output and system appraisals relate t…

• Assessing Quality of Experience in Natural Language Generation of German Text

  Dinh Nam Pham, Shushen Manakhimova, Vivien Macketanz, Sebastian Möller · 20 de agosto de 2026

  The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, such as large language models (LLMs), are now widely deployed in real-world applications. However, traditional automatic metrics fail to capture the …

• Clause Encounters of the Third Kind: Can LLMs Replace Language Teachers?

  Kristina Šekrst, Ana Kovačić · 18 de agosto de 2026

  While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback…

• TRACE-BN: Transferring Bangla-English Tutoring Behavior to a Sub-1B Offline Language Model

  Khan Raiyan Ibne Reza, Sanjana Aktar Maria, Mohammad Tushar Abdullah, Asfee Bhuiyan Leen, Sumaiya Tabassum Nimi · 18 de agosto de 2026

  Bangla-English tutoring requires more than producing a correct translation: learners also need explanations of grammar differences, awareness of their likely errors, and targeted practice. We present TRACE-BN, a curriculum-guided dataset of structured tutoring traces for Bangla-speaking learners of …

• Hidden Language Consistency Phenomena in Reasoning LLMs

  Muhammad Ali Shafique, Kelly Marchisio · 11 de agosto de 2026

  Multilingual reasoning models are commonly evaluated by whether they arrive at the correct answer, but not by whether they preserve the intended language while reasoning and responding. This omission conceals important multilingual behaviors that emerge as tasks become harder. In this paper, we stud…

• Measuring the Cross-Lingual Comprehension Gap: How the language of the evidence shapes what language models understand

  Rafael da Silva, Jeff Eicher · 10 de agosto de 2026

  Language models are often evaluated as though capabilities demonstrated in English remain equally available when the same content is presented in other languages. Traditional multilingual benchmarks rarely isolate language while holding content, question, reference answer, model, and evaluation unit…

• Mitigating Scoring Bias in LLM-as-a-Judge via Random Number Generation

  Yuma Asato, Kiyoaki Shirai, Natthawut Kertkeidkachorn · 10 de agosto de 2026

  Large Language Models (LLMs) are often used as evaluators of text quality, known as LLM-as-a-Judge, which can outperform conventional automatic evaluation metrics that rely on reference texts. However, LLM evaluators tend to generate particular scores regardless of the context of the evaluated text,…

• Example-Guided Prompting for Document-Level Text Simplification

  Marina Litvak, Ariel Perstin, Ilan Shtilman, Michael Färber · 7 de agosto de 2026

  Document-level text simplification requires large language models (LLMs) to rewrite complex documents while preserving meaning, readability, and discourse coherence. Although prompt-based LLMs have shown promising performance, they often produce inconsistent simplifications because textual instructi…

• Language Models Generalize to Human-like Word Order Preferences

  Amanda Popadich, Shane Steinert-Threlkeld · 6 de agosto de 2026

  A central question in language acquisition is whether linguistic biases can emerge from general learning mechanisms operating over underdetermined input. Artificial Language Learning (ALL) studies have shown that human learners reliably generalize beyond the evidence provided, including by preferrin…

• PlainMedScale: A Corpus of Multi-Level Simplified Medical Texts in German and English

  Bruno Brocai, Ilaria Papagno, Mayumi Ohta · 4 de agosto de 2026

  We introduce PlainMedScale, a topic-aligned medical corpus spanning four levels of comprehensibility in German and English, drawn from MSD (professional and consumer), Gesund.Bund, Apotheken Umschau Einfache Sprache, and the NHS. The four tiers correspond to distinct communicative functions --- refe…

• MORFES: A Benchmark for Productive Inflectional Competence in Modern Greek

  Ioakeim Perros, Cleopatra Papadopoulou, Ayoub Kirouane, Christos Petrocheilos · 31 de julio de 2026

  Modern Greek is a richly inflected language, yet the language models built for it are evaluated mainly on factual knowledge, and no benchmark is dedicated to their inflectional competence. We introduce MORFES (Morphological Open-class Recognition-and-Formation Evaluation Suite), a benchmark of 500 e…

• A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

  Kyuri Im, Michael F\"arber · 29 de julio de 2026

  Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language model (LLM)-based simplification of scientific texts and present a human-in-the-loop workflow that transforms expert summaries into more accessible vers…

• RALS: Resources and Baselines for Romanian Automatic Lexical Simplification

  Fabian Anghel, Petru Theodor Cristea, Claudiu Creanga, Sergiu Nisioi · 23 de julio de 2026

  We introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches. We propose a methodology for ordering simplification suggestions using a pairwise rankin…

• Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty

  Yiheng Wu, Jue Hou, Roman Yangarber · 22 de julio de 2026

  Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications. However, the development of robust assessment models is severely hindered by a critical bottleneck: the scarcity of expert-annotated corpora containing fine-grained d…

• Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models

  Netanel Eliav · 22 de julio de 2026

  Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (markdown, plain text, prose, or tabular), how many simultaneous instructions a system prompt can carry before compliance degrades, and how much context a model can…