Papers Text Simplification
“Text Simplification” 태그가 달린 논문 505편 · 필터 해제
GPS: 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 SimplificationReadability Measures and Automatic Text Simplification: In the Search of a Construct
Readability is a key concept in the current era of abundant written information. To help making texts more readable and make information more accessible to everyone, a line of researched aims at making texts accessible f…
Text SimplificationMaking Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification
The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer solutions, they too struggle with balancing improved readability against pr…
Text SimplificationOUNLP at TSAR 2025 Shared Task: Multi-Round Text Simplifier via Code Generation
This paper describes the OUNLP system submitted to the TSAR-2025 Shared Task (Alva-Manchego et al., 2025), designed for readability-controlled text simplification using LLM-prompting-based generation. Based on the analys…
Text SimplificationCode GenerationText Simplification with Sentence Embeddings
Sentence embeddings can be decoded to give approximations of the original texts used to create them. We explore this effect in the context of text simplification, demonstrating that reconstructed text embeddings preserve…
Text SimplificationDETECT: Determining Ease and Textual Clarity of German Text Simplifications
Current evaluation of German automatic text simplification (ATS) relies on general-purpose metrics such as SARI, BLEU, and BERTScore, which insufficiently capture simplification quality in terms of simplicity, meaning pr…
Text SimplificationLLM-Augmented Symbolic NLU System for More Reliable Continuous Causal Statement Interpretation
Despite the broad applicability of large language models (LLMs), their reliance on probabilistic inference makes them vulnerable to errors such as hallucination in generated facts and inconsistent output structure in nat…
Natural Language UnderstandingIncremental LearningText SimplificationToward Human-Centered Readability Evaluation
Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Process…
Text SimplificationFacilitating Cognitive Accessibility with LLMs: A Multi-Task Approach to Easy-to-Read Text Generation
Simplifying complex texts is essential for ensuring equitable access to information, especially for individuals with cognitive impairments. The Easy-to-Read (ETR) initiative offers a framework for making content accessib…
parameter-efficient fine-tuningMulti-Task LearningText SimplificationText SummarizationBeyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining
Most studies on language model pretraining focus on large datasets, leaving open questions about optimization in data-constrained settings. In such settings, the effects of training data order and of including alternativ…
Text SimplificationData AugmentationGerman4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German
The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German…
Text SimplificationJUDGEBERT: Assessing Legal Meaning Preservation Between Sentences
Simplifying text while preserving its meaning is a complex yet essential task, especially in sensitive domain applications like legal texts. When applied to a specialized field, like the legal domain, preservation differ…
Text SimplificationLLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText
In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employ…
Text SimplificationHallucination Detection and Mitigation in Scientific Text Simplification using Ensemble Approaches: DS@GT at CLEF 2025 SimpleText
In this paper, we describe our methodology for the CLEF 2025 SimpleText Task 2, which focuses on detecting and evaluating creative generation and information distortion in scientific text simplification. Our solution int…
Natural Language InferenceText SimplificationSemantic SimilarityAPIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. Consequently, several approaches have bee…
Grammatical Error CorrectionText SimplificationTHM@SimpleText 2025 -- Task 1.1: Revisiting Text Simplification based on Complex Terms for Non-Experts
Scientific text is complex as it contains technical terms by definition. Simplifying such text for non-domain experts enhances accessibility of innovation and information. Politicians could be enabled to understand new f…
Text SimplificationEvaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities
Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in generative AI, especially large language m…
Text SimplificationDocument-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require the understanding of longer context to generate high-quality texts. In this paper, we investig…
Document Level Machine TranslationImage CaptioningMachine TranslationSentence+2Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data
Paraphrasing re-expresses meaning to enhance applications like text simplification, machine translation, and question-answering. Specific paraphrase types facilitate accurate semantic analysis and robust language models.…
Machine TranslationParaphrase GenerationParaphrase IdentificationQuestion Answering+2Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss
Understanding health information is essential in achieving and maintaining a healthy life. We focus on simplifying health information for better understanding. With the availability of generative AI, the simplification p…
Missing ElementsSemantic SimilaritySemantic Textual SimilarityText Simplification