Papers Text Simplification
“Text Simplification” 태그가 달린 논문 504편 · 필터 해제
Reinforcement Learning for improving Large Language Models' Catalan text simplification capabilities
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 r…
Cross-Lingual TransferReinforcement LearningText SimplificationSimpCue: Cue-Based Prompting for Multilingual Text Simplification
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 addin…
Text SimplificationRALS: Resources and Baselines for Romanian Automatic Lexical Simplification
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 propo…
Text SimplificationAlign and Shine: Building High-Quality Sentence-Aligned Corpora for Multilingual Text Simplification
Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including language learners and readers with limited literacy. Despite its impor…
Text SimplificationTranslate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French
Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity and translation. This study investigates the effectiveness of differe…
Text SimplificationFrom Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text
The complexity of Vietnam's legal texts presents a significant barrier to public access to justice. While Large Language Models offer a promising solution for legal text simplification, evaluating their true capabilities…
Text SimplificationLegal ReasoningMuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator
As Large Language Models (LLMs) become increasingly prevalent in text simplification, systematically evaluating their outputs across diverse prompting strategies and architectures remains a critical methodological challe…
Text SimplificationRight at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification
Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constructing personalized parallel corpora is costly, while existing large lang…
Reinforcement LearningText SimplificationTaming CATS: Controllable Automatic Text Simplification through Instruction Fine-Tuning with Control Tokens
Controllable Automatic Text Simplification (CATS) produces user-tailored outputs, yet controllability is often treated as a decoding problem and evaluated with metrics that are not reflective to the measure of control. W…
Text SimplificationA Human-in/on-the-Loop Framework for Accessible Text Generation
Plain Language and Easy-to-Read formats in text simplification are essential for cognitive accessibility. Yet current automatic simplification and evaluation pipelines remain largely automated, metric-driven, and fail to…
Text SimplificationText GenerationA Multilingual Human Annotated Corpus of Original and Easy-to-Read Texts to Support Access to Democratic Participatory Processes
Being able to understand information is a key factor for a self-determined life and society. It is also very important for participating in democratic processes. The study of automatic text simplification is often limite…
Text SimplificationAutomatic Simplification of Common Vulnerabilities and Exposures Descriptions
Understanding cyber security is increasingly important for individuals and organizations. However, a lot of information related to cyber security can be difficult to understand to those not familiar with the topic. In th…
Text SimplificationControlling Reading Ease with Gaze-Guided Text Generation
The way our eyes move while reading can tell us about the cognitive effort required to process the text. In the present study, we use this fact to generate texts with controllable reading ease. Our method employs a model…
Text SimplificationText GenerationProfiling German Text Simplification with Interpretable Model-Fingerprints
While Large Language Models (LLMs) produce highly nuanced text simplifications, developers currently lack tools for a holistic, efficient, and reproducible diagnosis of their behavior. This paper introduces the Simplific…
Text SimplificationPrompt EngineeringSimplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs
Large language models (LLMs) enable strong text generation, and in general there is a practical tradeoff between fine-tuning and prompt engineering. We introduce Simplify-This, a comparative study evaluating both paradig…
Text SimplificationSemantic SimilarityPrompt EngineeringText GenerationEvaluating Small Decoder-Only Language Models for Grammar Correction and Text Simplification
Large language models have become extremely popular recently due to their ability to achieve strong performance on a variety of tasks, such as text generation and rewriting, but their size and computation cost make them …
Text SimplificationText GenerationUM_FHS at the CLEF 2025 SimpleText Track: Comparing No-Context and Fine-Tune Approaches for GPT-4.1 Models in Sentence and Document-Level Text Simplification
This work describes our submission to the CLEF 2025 SimpleText track Task 1, addressing both sentenceand document-level simplification of scientific texts. The methodology centered on using the gpt-4.1, gpt-4.1mini, and …
Text SimplificationPrompt EngineeringPIAST: Rapid Prompting with In-context Augmentation for Scarce Training data
LLMs are highly sensitive to prompt design, but handcrafting effective prompts is difficult and often requires intricate crafting of few-shot examples. We propose a fast automatic prompt construction algorithm that augme…
Text SimplificationPrompt EngineeringPERCS: Persona-Guided Controllable Biomedical Summarization Dataset
Automatic medical text simplification plays a key role in improving health literacy by making complex biomedical research accessible to diverse readers. However, most existing resources assume a single generic audience, …
Text SimplificationGPS: General Per-Sample Prompter
LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually remains challenging and time-consuming. Rec…
Reinforcement LearningText Simplification