mlsanityData to 14 Oct 2026

Retrieval augmentation of large language models for lay language generation

arXiv 2211.03818 ↗First posted 7 Nov 2022cs.CLcited by 21

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Abstract (arXiv)

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 37 of its 79 references that are in the map. First posted on arXiv 7 Nov 2022.

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37 of 79 references.

References · 37

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  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. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  7. 2017Billion-scale similarity search with GPUscited by 2,497
  8. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,565
  11. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  12. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
25 more
  1. 2020SummEval: Re-evaluating Summarization Evaluationcited by 674
  2. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
  3. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
  4. 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
  5. 2020TLDR: Extreme Summarization of Scientific Documentscited by 136
  6. 2016Neural Summarization by Extracting Sentences and Wordscited by 116
  7. 2019Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reportscited by 96
  8. 2021Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarizationcited by 76
  9. 2022Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturecited by 55
  10. 2018Learning to Summarize Radiology Findingscited by 54
  11. 2021Paragraph-level Simplification of Medical Textscited by 53
  12. 2020Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarizationcited by 52
  13. 2020Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structurescited by 49
  14. 2020Automated Lay Language Summarization of Biomedical Scientific Reviewscited by 46
  15. 2020Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymencited by 45
  16. 2021Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversationscited by 42
  17. 2022Evaluating Factuality in Text Simplificationcited by 34
  18. 2020Elaborative Simplification: Content Addition and Explanation Generation in Text Simplificationcited by 27
  19. 2021Literature-Augmented Clinical Outcome Predictioncited by 20
  20. 2022Improving Biomedical Information Retrieval with Neural Retrieverscited by 20
  21. 2022Readability Controllable Biomedical Document Summarizationcited by 19
  22. 2022A Dataset for Plain Language Adaptation of Biomedical Abstractscited by 19
  23. 2022Automatic Text Summarization Methods: A Comprehensive Reviewcited by 17
  24. 2022Entity-driven Fact-aware Abstractive Summarization of Biomedical Literaturecited by 3
  25. 2022PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directionscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 9; 2026: 2.

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  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  2. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  3. 2023APPLS: Evaluating Evaluation Metrics for Plain Language Summarizationcited by 15
  4. 2023Multilingual Simplification of Medical Textscited by 13
  5. 2024A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judgecited by 9
  6. 2024Clinical Insights: A Comprehensive Review of Language Models in Medicinecited by 6
  7. 2025Evaluating the Evaluators: Are readability metrics good measures of readability?cited by 6
  8. 2025A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarizationcited by 5
  9. 2025Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptationcited by 5
  10. 2025PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarizationcited by 4
  11. 2025LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Loadcited by 4
  12. 2024Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbotscited by 3
9 more
  1. 2025Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasingcited by 3
  2. 2024Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmologycited by 2
  3. 2025Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluationcited by 2
  4. 2023Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summariescited by 0
  5. 2024Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Reviewcited by 0
  6. 2025Large language models management of medications: three performance analysescited by 0
  7. 2025Not too long do read: Evaluating LLM-generated extreme scientific summariescited by 0
  8. 2026Improving Health Literacy through Lay Summarization of Radiological Reports: An Evaluation of BioNER and Retrieval-Augmented Generationcited by 0
  9. 2026Hierarchical Wasserstein Merging for Multi-Domain Multi-Task Learning: From Specialists to a Generalistcited by 0

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