mlsanityData to 14 Oct 2026

Simplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs

arXiv 2601.05794 ↗First posted 9 Jan 2026cs.CL cs.LGcited by 1

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Abstract (arXiv)

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 paradigms for text simplification with encoder-decoder LLMs across multiple benchmarks, using a range of evaluation metrics. Fine-tuned models consistently deliver stronger structural simplification, whereas prompting often attains higher semantic similarity scores yet tends to copy inputs. A human evaluation favors fine-tuned outputs overall. We release code, a cleaned derivative dataset used in our study, checkpoints of fine-tuned models, and prompt templates to facilitate reproducibility and future work.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 9 Jan 2026.

Builds on

25 of 42 references.

References · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  6. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  7. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  8. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  9. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  10. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  11. 2015Teaching Machines to Read and Comprehendcited by 1,049
  12. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 222
13 more
  1. 2015A Neural Conversational Modelcited by 196
  2. 2020Reformulating Unsupervised Style Transfer as Paraphrase Generationcited by 183
  3. 2017Sentence Simplification with Deep Reinforcement Learningcited by 109
  4. 2020ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformationscited by 101
  5. 2022LENS: A Learnable Evaluation Metric for Text Simplificationcited by 54
  6. 2019EASSE: Easier Automatic Sentence Simplification Evaluationcited by 50
  7. 2023BLESS: Benchmarking Large Language Models on Sentence Simplificationcited by 35
  8. 2022Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurementscited by 22
  9. 2023Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Textscited by 16
  10. 2023Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSAcited by 15
  11. 2025Progressive Document-level Text Simplification via Large Language Modelscited by 5
  12. 2024An In-depth Evaluation of Large Language Models in Sentence Simplification with Error-based Human Assessmentcited by 4
  13. 2023Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Modelscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Example-Guided Prompting for Document-Level Text Simplificationcited by 0