Paper Citation Record · LEDGER

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.10409.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.10409 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:13:33.019223Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:01:49.749076Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T15:26:09.024024Z

Reference resolution

46 of 46 outbound references displayed

verified exact1
verified fuzzy21
unresolved22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a251bac-55d7-4343-aff0-c520ffff1058 · outbound

This paper cites & Singh, J.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Singh, J

Reference 1

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Observation 8c7d6d92-b945-465d-9ca9-578c383a675a · outbound

This paper cites Clearly communicating research results across the clinical trials continuum (2025).

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Clearly communicating research results across the clinical trials continuum (2025)

Reference 2

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Observation e1311803-f8ab-4739-bf68-1302ee22ad73 · outbound

This paper cites & Cohen, T.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Cohen, T

Reference 3

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

Reference 4

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Observation 7f5d12ca-3656-4e70-9868-339c0ade21f7 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 5

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Observation 8b93eb4e-92e0-4167-8d54-a715f1e807e3 · outbound

This paper cites & Cohen, T.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Cohen, T

Reference 6

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Observation c3420ebb-32b0-490f-badb-f9e5f484db87 · outbound

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

Reference 7

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Observation d27d5297-2caa-4bfa-9e2f-9a14c5b7a811 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 8

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Observation f52cd0ba-9afb-434e-b8f7-494a4c71dbb7 · outbound

This paper cites ChatGPT as a Factual Inconsistency Evaluator for Text Summarization.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation ChatGPT as a Factual Inconsistency Evaluator for Text Summarization

Reference 9

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Kilicoglu, H

Reference 10

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Observation 3ac5f3c4-54a7-4ee2-a5e1-304211e06db6 · outbound

This paper cites Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles

Reference 11

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This paper cites & Shukla, A.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Shukla, A

Reference 12

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Leroy, G

Reference 13

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

Reference 14

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Demner-Fushman, D

Reference 15

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This paper cites Rouge: A package for automatic evaluation of summaries.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Rouge: A package for automatic evaluation of summaries

Reference 16

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Observation 7b96aa94-2420-4c7b-90c3-58662489f25a · outbound

This paper cites BLEU is Not Suitable for the Evaluation of Text Simplification.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation BLEU is Not Suitable for the Evaluation of Text Simplification

Reference 17

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

Reference 18

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Observation a5b9b581-c673-429b-8fa8-99a6309634aa · outbound

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Venditti, J

Reference 19

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Observation fc3859c8-fc9b-47e5-b627-90da0c8f50be · outbound

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Martin, M

Reference 20

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This paper cites G., Nettle, D.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation G., Nettle, D

Reference 21

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This paper cites Retrieval augmentation of large language models for lay language generation.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Retrieval augmentation of large language models for lay language generation

Reference 22

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Observation 7cbc7a52-5f92-42c0-9405-75b3a48c73ce · outbound

This paper cites Quality aspects of annotated data: A research synthesis.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Quality aspects of annotated data: A research synthesis

Reference 23

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

Reference 24

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This paper cites Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

Reference 25

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation X., Park, J

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Baram-Tsabari, A

Reference 28

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Gardner, J

Reference 29

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Simon, S

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Gpt-4 technical report

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Feng, S

Reference 38

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Zhu, W.-J

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Lavie, A

Reference 40

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Callison-Burch, C

Reference 41

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Observation a625bac8-e2a5-432e-8c65-9933dff816eb · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation BERTScore: Evaluating Text Generation with BERT

Reference 42

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation LENS: A Learnable Evaluation Metric for Text Simplification

Reference 43

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Roth, D

Reference 44

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation Unresolved cited work

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Observation 780aecd3-5aec-4828-a324-3102ac8099ed · outbound

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Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation & Specia, L

Reference 46

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Pith citing papers

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ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost? cites this paper.

ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost? Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation

Reference 50

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