Pith. sign in

REVIEW 4 major objections 4 minor 79 references

Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The best AI plain-language rewrites of biomedical abstracts matched human accuracy and completeness, but not simplicity or brevity.

desk verdict Useful shared-task infrastructure with real new data, but the abstract overstates the evidence on the simplicity and brevity half of the headline claim. read the letter →

arxiv 2507.14096 v2 pith:2OXZXVZZ submitted 2025-07-18 cs.CL cs.AIcs.IR

Brian Ondov , William Xia , Kush Attal , Ishita Unde , Jerry He , Dina Demner-Fushman This is my paper
classification cs.CLcs.AIcs.IR
keywords plainlanguageadaptationbiomedicaltextsimplificationlargemodelssharedtaskevaluationmanualautomaticmetricspatient-facingmedical
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports what a two-year shared task on plain-language adaptation of biomedical abstracts revealed about large language models. The central claim is that top-performing systems rivaled human reference writers on factual accuracy and completeness when rewriting abstracts sentence by sentence, but did not match human simplicity or brevity. The paper also claims that automatic reference-based metrics such as SARI, SAMSA, and BLEU correlate poorly with expert manual judgments, with BERTScore correlating best. If these claims hold, current language models can already support accurate patient-facing summaries of medical literature, but evaluating them requires expert review rather than standard automatic scoring.

What carries the argument

The machinery is the PLABA task design itself: a sentence-aligned rewriting task in which each source sentence of a biomedical abstract has a corresponding plain-language output, evaluated on separate axes rather than one overall score. The organizers built four professionally written references per abstract for automatic scoring, an automatic sentence-alignment pipeline for document-level systems, and a manual evaluation rubric that judged simplicity and accuracy in 2023, streamlined to simplicity, accuracy, completeness, and brevity in 2024. The argument for the central claim runs by contrasting top system scores against the human reference average on these manual axes, then correlating each automatic metric with the manual scores.

What would settle it

Re-score the same system outputs with a fresh panel of biomedical expert annotators using the same rubrics; if the new rankings place different systems above the human reference average, or if sentence-simplicity agreement remains near the reported low level, the central claim about rivaling humans does not replicate. A complementary behavioral check would give lay readers either the top system outputs or the original abstracts and test comprehension: if readers understand the original no worse, the practical value of the adaptation is not established.

Watch

Extended reading notes

Core claim

On its own terms, the paper's core discovery is that sentence-level plain-language adaptation of biomedical abstracts is largely effective for factual content but not for readability. In the 2023 manual evaluation, the top system scored 97.65 for accuracy and 98.72 for completeness against a human average of 96.21 and 96.16, while its simplicity score of 91.51 fell below the human average of 93.56. In 2024, the best systems pushed accuracy and completeness even higher, and large-language-model-based term replacements in Task 2 also earned high manual scores for accuracy, completeness, and simplicity, though not brevity. Across both years, systems struggled to identify difficult terms and to classify the right replacement strategy. The paper further reports that reference-based automatic metrics generally did not align with manual judgments: SARI and SAMSA had the worst correlations, and BERTScore had the best.

Load-bearing premise

The load-bearing premise is that the contracted biomedical experts' manual ratings are reliable ground truth; the paper itself reports low inter-annotator agreement on sentence simplicity and negative agreement on fluency, so if those ratings are noisy the headline comparisons and metric correlations could shift.

Editorial extensions

If this is right

  • Current instruction-tuned language models, prompted with abstract context, can generate plain-language versions of biomedical abstracts that preserve factual content and completeness at the level of human-written adaptations.
  • Because the top systems lagged on simplicity and brevity, further gains require explicit optimization of readability, not just factual fidelity.
  • Reference-based automatic metrics are not reliable ranking tools for this task, so shared tasks in patient-facing medical text should budget for manual evaluation.
  • Term-level adaptation is the harder subproblem: identifying difficult terms and choosing replacement strategies scored much lower than generating the replacements themselves.
  • Document-level systems can participate through automatic sentence alignment, but the alignment cannot handle sentence transposition or merging, which limits some rewriting strategies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, if the poor metric correlations generalize outside this track, new automatic evaluation for simplification should be validated sentence-level against expert judgments before being used in medical settings.
  • Beyond the paper, the human baseline here came from reference adaptations written for the dataset; a stronger practical test would compare system outputs against clinicians' real plain-language explanations through a comprehension study with patients.
  • Beyond the paper, the low inter-annotator agreement on sentence simplicity suggests the simplicity gap may partly be an artifact of rubric ambiguity, and a sharper definition of brevity versus simplicity could change system rankings.
  • Beyond the paper, given the documented hallucinations, a targeted automatic check that verifies numerical results and named entities against the source sentence could catch the most dangerous errors even when overall accuracy looks high.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper reports on the Plain Language Adaptation of Biomedical Abstracts (PLABA) shared task at TREC 2023 and 2024. Task 1 asked teams to rewrite biomedical abstracts sentence-by-sentence into plain language; Task 2 asked teams to identify, classify, and generate replacements for difficult terms. The paper describes the data, the manual and automatic evaluation protocols, the participating systems, and the results, and it draws conclusions about the state of LLM-based plain language adaptation, the reliability of automatic metrics, and the risk of hallucinations.

Significance. If the reported findings hold, the paper is a substantial empirical contribution to evaluation methodology for biomedical text simplification. Its concrete strengths include a two-year shared task with twelve participating teams and thirty-eight runs, a four-fold professionally written reference set for the 2023 Task 1 test set, extensive manual evaluation of system outputs, and transparent appendices documenting inter-annotator agreement, the sentence-alignment pipeline, system metadata, and hallucination examples. The correlation analysis in Section 5.1.4 provides useful evidence that n-gram-based metrics, including SARI and SAMSA, correlate poorly with manual judgments, while BERTScore correlates more strongly. The paper's headline claims are falsifiable and are presented with enough table-level detail for the reader to inspect the supporting evidence.

major comments (4)
  1. [Abstract; §5.2, Table 4] The headline claim 'but not brevity' has no human baseline. Brevity (BRV) was introduced only in the 2024 offering (§4.2), and §3.1.3 states that no manually written reference adaptations were created for the 2024 test set; Table 4 therefore lists BRV scores only for system submissions, with no Manual_avg row. Without a human reference score or a pre-specified absolute criterion, the data cannot support the comparative claim that systems did not rival humans on brevity.
  2. [Appendix F; Table 3; §4.2] The 'not simplicity' half of the headline rests on the SIM composite, which averages SEN, TRM, TAC, and FLU. Appendix F reports sentence-level Krippendorff's alpha of 0.1748 for SEN and -0.0255 for FLU, and §4.2 explains that SEN was conflated with brevity and that FLU had near-perfect scores. The 2.05-point SIM gap between PLABA_1 (91.51) and Manual_avg (93.56) in Table 3 is therefore within the range that annotation noise could explain, yet no confidence intervals, significance tests, or sensitivity analyses are provided. The authors should either report reliability-aware comparisons or explicitly qualify the simplicity claim.
  3. [§5.1.3; §6.3; Table 3] The positive half of the headline, that top-performing models rivaled human levels of factual accuracy and completeness, is supported by a single run: PLABA_1 in Round 2 of TREC 2023 exceeded Manual_avg on ACC and COM, while other systems in Table 3 scored below Manual_avg on those axes, and no human comparator is available for 2024. The plural 'top-performing models' overgeneralizes; the claim should be restricted to the single best run in that round unless additional supporting evidence is provided.
  4. [§5.1.4; Appendix G] The metric-correlation analysis uses Pearson correlations on sentence-level averages of a 3-point Likert scale, which produces the striations visible in Appendix G. Pearson r on discrete, bounded data can be sensitive to the chosen aggregation and scale assumptions; reporting Spearman's rho or an ordinal association measure would make the ranking of metrics (SARI and SAMSA lowest, BERTScore highest) more robust. This is a methodological concern rather than a reason to doubt the qualitative conclusion that most reference-based metrics correlate poorly.
minor comments (4)
  1. [§5.1.2] The sentence 'we omit SAMSA due to lack of poor correlation with manual judgments' appears to have a missing negation; based on §5.1.4 and Appendix G it should read 'due to its poor correlation with manual judgments.'
  2. [§3.2.2] The text says 'Table 1d displays counts of each simplification type,' but the counts appear in Figure 1(d); Table 1 contains the manual evaluation axes. Please correct the cross-reference.
  3. [Introduction; References] The introduction cites Karamcheti et al. (2021) for Mistral, but that reference describes a Stanford CRFM project named Mistral, not the Mistral AI models used later in the paper (e.g., Mistral-Nemo-Instruct-2407 in Appendix D). Please correct or disambiguate the citation.
  4. [Figure 1(b) caption] The caption says 'an PLABA abstract pair'; this should be 'a PLABA abstract pair.'

Circularity Check

0 steps flagged · score 0.0 of 10

No material circularity: the paper is an empirical shared-task evaluation whose headline findings rest on new manual judgments and metric correlations, not on fitted parameters, self-citation chains, or definitional reductions.

full rationale

This is an empirical shared-task report: it reports automatic metric scores, manual Likert-scale judgments, and correlations between them, and it derives no result from a fitted equation or from a prior result whose truth would force the finding. The self-citations (Attal et al. 2023 for the PLABA dataset, Ondov et al. 2022 for the survey, Ondov and Demner-Fushman 2024 for the SCER training technique, and Xia et al. 2025 for Task 2 data details) are data and tool provenance, not load-bearing premises. The headline comparison to human performance uses manually written reference adaptations as a human baseline; although those references were created by the organizers, the top-performing systems compared against them (e.g., PLABA_1) were not fitted to those references, and the manual evaluation rubric was applied to both system outputs and reference outputs, so the comparison is not circular by construction. The limitations the paper itself flags (Appendix F reports sentence-level Krippendorff's alpha of 0.1748 for SEN and -0.0255 for FLU, and section 5.2 notes that BRV had no manually written reference baseline) are genuine threats to the reliability of the 'not simplicity or brevity' half of the headline, but a noisy or partial measurement is not a conclusion that reduces to its own inputs. No uniqueness theorem, ansatz-by-citation, fitted-parameter-as-prediction, or renaming-of-known-result appears in the derivation chain, so no circular step is present.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

No model parameters or scientific constants are fitted in this paper. The only hand-chosen numeric evaluation parameter is the 75% span-overlap threshold for Task 2A. All other assumptions concern the validity of manual evaluation, reference sets, alignment, and sampling, as listed above. The Task 2 annotation taxonomy is a coding scheme rather than an invented entity.

free parameters (1)
  • Task 2A span-overlap threshold = 0.75
    Systems must share at least 75% of a reference span to count as correct (Section 3.2.2). This hand-chosen value affects reported Task 2A F1 scores.
assumptions (5)
  • domain assumption Manual evaluations by contracted science writers on a 3-point Likert scale are a valid gold standard for simplicity, accuracy, completeness, and brevity.
    All headline comparisons rest on these judgments (Sections 4.1.2 and 4.2). Appendix F reports low sentence-level inter-annotator agreement for sentence simplicity (alpha = 0.1748) and fluency (alpha = -0.0255), so this assumption is partially load-bearing.
  • domain assumption Linear interpolation of Likert scores (-1, 0, 1) to a 0 to 100 scale preserves meaningful comparisons.
    The paper converts the 3-point ordinal scale to 0 to 100 for reporting (Sections 4.1.2 and 4.3.2). This assumes equal intervals between the ordinal points.
  • domain assumption Four professional reference adaptations per source abstract are sufficient to make reference-based automatic metrics meaningful.
    The 2023 test set used four references per abstract (Section 3.1.3); the metric correlation results in Figure 3a depend on this reference set.
  • domain assumption The automatic sentence-alignment pipeline (Vecalign with LASER embeddings plus the assignment algorithm) does not systematically distort evaluation of document-level submissions.
    Used for document-level submissions in Task 1 (Section 4.1.3 and Appendix A); the paper notes limitations such as no support for sentence transposition (Section 6.1).
  • domain assumption The 40-abstract manual evaluation set and the 3-sentence accuracy subset for TREC 2023 are representative of the full 400-abstract test set.
    Simplicity was judged on 40 abstracts and accuracy on a subset of three sentences per abstract (Section 4.1.2); the correlation results in Section 5.1.4 are based on this sample.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track." pith.science (2026). https://pith.science/paper/2OXZXVZZ

@misc{pith2026250714096,
  author       = {Pith},
  title        = {Pith review of: Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OXZXVZZ}},
  note         = {Machine review of arXiv:2507.14096}
}
read the original abstract

Objective: Recent advances in language models have shown potential to adapt professional-facing biomedical literature to plain language, making it accessible to patients and caregivers. However, their unpredictability, combined with the high potential for harm in this domain, means rigorous evaluation is necessary. Our goals with this track were to stimulate research and to provide high-quality evaluation of the most promising systems. Methods: We hosted the Plain Language Adaptation of Biomedical Abstracts (PLABA) track at the 2023 and 2024 Text Retrieval Conferences. Tasks included complete, sentence-level, rewriting of abstracts (Task 1) as well as identifying and replacing difficult terms (Task 2). For automatic evaluation of Task 1, we developed a four-fold set of professionally-written references. Submissions for both Tasks 1 and 2 were provided extensive manual evaluation from biomedical experts. Results: Twelve teams spanning twelve countries participated in the track, with models from multilayer perceptrons to large pretrained transformers. In manual judgments of Task 1, top-performing models rivaled human levels of factual accuracy and completeness, but not simplicity or brevity. Automatic, reference-based metrics generally did not correlate well with manual judgments. In Task 2, systems struggled with identifying difficult terms and classifying how to replace them. When generating replacements, however, LLM-based systems did well in manually judged accuracy, completeness, and simplicity, though not in brevity. Conclusion: The PLABA track showed promise for using Large Language Models to adapt biomedical literature for the general public, while also highlighting their deficiencies and the need for improved automatic benchmarking tools.

Figures

Figures reproduced from arXiv: 2507.14096 by the authors.

Figure 1
Figure 1. PLABA tasks. In (a), an excerpt of a PLABA abstract and its manually written [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Radar charts of manual evaluation results for Task 1 at (a) TREC 2023 and (b) [PITH_FULL_IMAGE:figures/full_fig_p019_2.png] view at source ↗
Figure 3
Figure 3. In (a), correlation of automatic metrics with manually evaluated Simplicity [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

79 extracted references · 66 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  3. [3]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  4. [4]

    , author Azmi, A.M

    author Al-Thanyyan, S.S. , author Azmi, A.M. , year 2021 . title Automated text simplification: A survey . journal ACM Computing Surveys (CSUR) volume 54 , pages 1--36

    work page 2021

  5. [5]

    , author Alobeidli, H

    author Almazrouei, E. , author Alobeidli, H. , author Alshamsi, A. , author Cappelli, A. , author Cojocaru, R. , author Debbah, M. , author Goffinet, \'E . , author Hesslow, D. , author Launay, J. , author Malartic, Q. , et al., year 2023 . title The falcon series of open language models . journal arXiv preprint arXiv:2311.16867

    arXiv 2023

  6. [6]

    , author Martin, L

    author Alva-Manchego, F. , author Martin, L. , author Scarton, C. , author Specia, L. , year 2019 . title Easse: Easier automatic sentence simplification evaluation , in: booktitle Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP):...

    work page 2019

  7. [7]

    , author Scarton, C

    author Alva-Manchego, F. , author Scarton, C. , author Specia, L. , year 2021 . title The (un) suitability of automatic evaluation metrics for text simplification . journal Computational Linguistics volume 47 , pages 861--889

    work page 2021

  8. [8]

    , author Schwenk, H

    author Artetxe, M. , author Schwenk, H. , year 2019 . title Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond . journal Transactions of the Association for Computational Linguistics volume 7 , pages 597--610

    work page 2019

Show all 79 references
  1. [9]

    , author Ondov, B

    author Attal, K. , author Ondov, B. , author Demner-Fushman, D. , year 2023 . title A dataset for plain language adaptation of biomedical abstracts . journal Scientific Data volume 10 , pages 8

    2023

  2. [10]

    , author Sips, R.J

    author Van den Bercken, L. , author Sips, R.J. , author Lofi, C. , year 2019 . title Evaluating neural text simplification in the medical domain , in: booktitle The World Wide Web Conference , pp. pages 3286--3292

    2019

  3. [11]

    , author Sheridan, S.L

    author Berkman, N.D. , author Sheridan, S.L. , author Donahue, K.E. , author Halpern, D.J. , author Crotty, K. , year 2011 . title Low health literacy and health outcomes: an updated systematic review . journal Annals of internal medicine volume 155 , pages 97--107

    2011

  4. [12]

    , author Mann, B

    author Brown, T. , author Mann, B. , author Ryder, N. , author Subbiah, M. , author Kaplan, J.D. , author Dhariwal, P. , author Neelakantan, A. , author Shyam, P. , author Sastry, G. , author Askell, A. , et al., year 2020 . title Language models are few-shot learners . journa...

    2020

  5. [13]

    , year 2020

    author Cook, T. , year 2020 . title Bert english uncased bigrams . https://www.kaggle.com/datasets/toddcook/bert-english-uncased-bigrams

    2020

  6. [14]

    , author Rogers, W.J

    author Demner-Fushman, D. , author Rogers, W.J. , author Aronson, A.R. , year 2017 . title Metamap lite: an evaluation of a new java implementation of metamap . journal Journal of the American Medical Informatics Association volume 24 , pages 841--844

    2017

  7. [15]

    , author Marshall, I

    author Devaraj, A. , author Marshall, I. , author Wallace, B.C. , author Li, J.J. , year 2021 . title Paragraph-level simplification of medical texts , in: booktitle Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguisti...

    2021

  8. [16]

    , author Chang, M

    author Devlin, J. , author Chang, M. , author Lee, K. , author Toutanova, K. , year 2018 . title BERT: pre-training of deep bidirectional transformers for language understanding . journal CoRR volume abs/1810.04805 . http://arxiv.org/abs/1810.04805, http://arxiv.org/abs/1810.0...

    2018 arXiv

  9. [17]

    , author Vezzani, F

    author Di Nunzio, G.M. , author Vezzani, F. , author Bonato, V. , author Azarbonyad, H. , author Kamps, J. , author Ermakova, L. , et al., year 2024 . title Overview of the clef 2024 simpletext task 2: identify and explain difficult concepts . journal Working Notes of CLEF

    2024

  10. [18]

    , author Jauhri, A

    author Dubey, A. , author Jauhri, A. , author Pandey, A. , author Kadian, A. , author Al-Dahle, A. , author Letman, A. , author Mathur, A. , author Schelten, A. , author Yang, A. , author Fan, A. , et al., year 2024 . title The llama 3 herd of models . journal arXiv preprint a...

    2024 arXiv

  11. [19]

    , author Kabongo, S

    author D’Souza, J. , author Kabongo, S. , author Giglou, H.B. , author Zhang, Y. , year 2024 . title Overview of the clef 2024 simpletext task 4: Sota? tracking the state-of-the-art in scholarly publications , in: booktitle Working Notes of the Conference and Labs of the Evalu...

    2024

  12. [20]

    , author Azarbonyad, H

    author Ermakova, L. , author Azarbonyad, H. , author Bertin, S. , author Augereau, O. , year 2023 a. title Overview of the clef 2023 simpletext task 2: Difficult concept identification and explanation. , in: booktitle CLEF (Working Notes) , pp. pages 2835--2854

    2023

  13. [21]

    , author Bellot, P

    author Ermakova, L. , author Bellot, P. , author Braslavski, P. , author Kamps, J. , author Mothe, J. , author Nurbakova, D. , author Ovchinnikova, I. , author Sanjuan, E. , year 2021 . title Overview of simpletext clef 2021 workshop and pilot tasks , in: booktitle 12th Confer...

    2021

  14. [22]

    , author Bertin, S

    author Ermakova, L. , author Bertin, S. , author McCombie, H. , author Kamps, J. , et al., year 2023 b. title Overview of the clef 2023 simpletext task 3: Simplification of scientific texts. , in: booktitle CLEF (Working Notes) , pp. pages 2855--2875

    2023

  15. [23]

    , author Laim \'e , V

    author Ermakova, L. , author Laim \'e , V. , author McCombie, H. , author Kamps, J. , year 2024 a. title Overview of the clef 2024 simpletext task 3: Simplify scientific text , in: booktitle Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024) , pp. pag...

    2024

  16. [24]

    , author Ovchinnikova, I

    author Ermakova, L. , author Ovchinnikova, I. , author Kamps, J. , author Nurbakova, D. , author Ara \'u jo, S. , author Hannachi, R. , year 2022 a. title Overview of the clef 2022 simpletext task 2: complexity spotting in scientific abstracts , in: booktitle Proceedings of th...

    2022

  17. [25]

    , author Ovchinnikova, I

    author Ermakova, L. , author Ovchinnikova, I. , author Kamps, J. , author Nurbakova, D. , author Ara \'u jo, S. , author Hannachi, R. , author Poinsu, N. , year 2022 b. title Overview of the clef 2022 simpletext task 3: Query biased simplification of scientific texts , in: boo...

    2022

  18. [26]

    , author SanJuan, E

    author Ermakova, L. , author SanJuan, E. , author Huet, S. , author Azarbonyad, H. , author Augereau, O. , author Kamps, J. , year 2023 c. title Overview of the clef 2023 simpletext lab: automatic simplification of scientific texts , in: booktitle International Conference of t...

    2023

  19. [27]

    , author SanJuan, E

    author Ermakova, L. , author SanJuan, E. , author Huet, S. , author Azarbonyad, H. , author Di Nunzio, G.M. , author Vezzani, F. , author D’Souza, J. , author Kamps, J. , year 2024 b. title Overview of the clef 2024 simpletext track: Improving access to scientific texts for ev...

    2024

  20. [28]

    , author Sanjuan, E

    author Ermakova, L. , author Sanjuan, E. , author Kamps, J. , author Huet, S. , author Ovchinnikova, I. , author Nurbakova, D. , author Ara \'u jo, S. , author Hannachi, R. , author Mathurin, E. , author Bellot, P. , year 2022 c. title Overview of the clef 2022 simpletext lab:...

    2022

  21. [29]

    , author Huang, H

    author Flores, L.J.Y. , author Huang, H. , author Shi, K. , author Chheang, S. , author Cohan, A. , year 2023 . title Medical text simplification: Optimizing for readability with unlikelihood training and reranked beam search decoding , in: booktitle Findings of the Associatio...

    2023

  22. [30]

    , author Kra, P

    author Friedman, C. , author Kra, P. , author Rzhetsky, A. , year 2002 . title Two biomedical sublanguages: a description based on the theories of zellig harris . journal Journal of Biomedical Informatics volume 35 , pages 222--235

    2002

  23. [31]

    , author Ramachandran, G.K

    author Gangavarapu, H. , author Ramachandran, G.K. , author Lybarger, K. , author Yetisgen, M. , author Uzuner, \"O . , year 2025 . title Adapting biomedical abstracts into plain language using large language models . journal arXiv preprint arXiv:2501.15700

    2025 arXiv

  24. [32]

    , author Luo, Z

    author Goldsack, T. , author Luo, Z. , author Xie, Q. , author Scarton, C. , author Shardlow, M. , author Ananiadou, S. , author Lin, C. , year 2023 . title Overview of the biolaysumm 2023 shared task on lay summarization of biomedical research articles , in: booktitle The 22n...

    2023

  25. [33]

    , author Scarton, C

    author Goldsack, T. , author Scarton, C. , author Shardlow, M. , author Lin, C. , year 2024 . title Overview of the biolaysumm 2024 shared task on the lay summarization of biomedical research articles , in: booktitle Proceedings of the 23rd Workshop on Biomedical Natural Langu...

    2024

  26. [34]

    , author Miller, R.A

    author Goodman, K.W. , author Miller, R.A. , year 2021 . title Ethics in biomedical and health informatics: users, standards, and outcomes , in: booktitle Biomedical informatics: Computer applications in health care and biomedicine . publisher Springer , pp. pages 391--423

    2021

  27. [35]

    , author Liu, X

    author He, P. , author Liu, X. , author Gao, J. , author Chen, W. , year 2021 . title Deberta: Decoding-enhanced bert with disentangled attention , in: booktitle International Conference on Learning Representations . https://openreview.net/forum?id=XPZIaotutsD

    2021

  28. [36]

    , author Shen, Y

    author Hu, E.J. , author Shen, Y. , author Wallis, P. , author Allen-Zhu, Z. , author Li, Y. , author Wang, S. , author Wang, L. , author Chen, W. , et al., year 2022 . title Lora: Low-rank adaptation of large language models. journal ICLR volume 1 , pages 3

    2022

  29. [37]

    , author McCandlish, S

    author Kaplan, J. , author McCandlish, S. , author Henighan, T. , author Brown, T.B. , author Chess, B. , author Child, R. , author Gray, S. , author Radford, A. , author Wu, J. , author Amodei, D. , year 2020 . title Scaling laws for neural language models . journal arXiv pre...

    2020 arXiv

  30. [38]

    , author Orr, L

    author Karamcheti, S. , author Orr, L. , author Bolton, J. , author Zhang, T. , author Goel, K. , author Narayan, A. , author Bommasani, R. , author Narayanan, D. , author Hashimoto, T. , author Jurafsky, D. , author Manning, C.D. , author Potts, C. , author Ré, C. , author Li...

    2021

  31. [39]

    , author Fishburne Jr, R.P

    author Kincaid, J.P. , author Fishburne Jr, R.P. , author Rogers, R.L. , author Chissom, B.S. , year 1975 . title Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel

    1975

  32. [40]

    , author Panzer, A.M

    author Kindig, D.A. , author Panzer, A.M. , author Nielsen-Bohlman, L. , year 2004 . title Health Literacy: A Prescription to End Confusion . publisher National Academies Press

    2004

  33. [41]

    , year 2010

    author King, A. , year 2010 . title Poor health literacy: a 'hidden' risk factor . journal Nature Reviews Cardiology volume 7 , pages 473--474

    2010

  34. [42]

    , author Razniewski, S

    author Knappich, V. , author Razniewski, S. , author Friedrich, A. , year 2023 . title Boschai@ plaba 2023: Leveraging edit operations in end-to-end neural sentence simplification . journal arXiv preprint arXiv:2311.01907

    2023 arXiv

  35. [43]

    , author Kopitar, L

    author Kocbek, P. , author Kopitar, L. , author Zhang, Z. , author Aydin, E. , author Topaz, M. , author Stiglic, G. , year 2025 . title Um\_fhs at trec 2024 plaba: Exploration of fine-tuning and ai agent approach for plain language adaptations of biomedical text . journal arX...

    2025 arXiv

  36. [44]

    , author Yoon, W

    author Lee, J. , author Yoon, W. , author Kim, S. , author Kim, D. , author Kim, S. , author So, C.H. , author Kang, J. , year 2020 . title Biobert: a pre-trained biomedical language representation model for biomedical text mining . journal Bioinformatics volume 36 , pages 1234--1240

    2020

  37. [45]

    , author Liu, Y

    author Lewis, M. , author Liu, Y. , author Goyal, N. , author Ghazvininejad, M. , author Mohamed, A. , author Levy, O. , author Stoyanov, V. , author Zettlemoyer, L. , year 2020 a. title Bart: Denoising sequence-to-sequence pre-training for natural language generation, transla...

    2020

  38. [46]

    u ttler, H. , author Lewis, M. , author Yih, W.t. , author Rockt \

    author Lewis, P. , author Perez, E. , author Piktus, A. , author Petroni, F. , author Karpukhin, V. , author Goyal, N. , author K \"u ttler, H. , author Lewis, M. , author Yih, W.t. , author Rockt \"a schel, T. , et al., year 2020 b. title Retrieval-augmented generation for kn...

    2020

  39. [47]

    , author Belkadi, S

    author Li, Z. , author Belkadi, S. , author Micheletti, N. , author Han, L. , author Shardlow, M. , author Nenadic, G. , year 2024 a. title Beemanc at the plaba track of tac-2023: Investigating llms and controllable attributes for improving biomedical text readability . journa...

    2024

  40. [48]

    , author Belkadi, S

    author Li, Z. , author Belkadi, S. , author Micheletti, N. , author Han, L. , author Shardlow, M. , author Nenadic, G. , year 2024 b. title Large language models for biomedical text simplification: Promising but not there yet . journal arXiv preprint arXiv:2408.03871

    2024 arXiv

  41. [49]

    , year 2004

    author Lin, C.Y. , year 2004 . title Rouge: A package for automatic evaluation of summaries , in: booktitle Text summarization branches out , pp. pages 74--81

    2004

  42. [50]

    , author Humphreys, B.L

    author Lindberg, D.A. , author Humphreys, B.L. , author McCray, A.T. , year 1993 . title The unified medical language system . journal Yearbook of medical informatics volume 2 , pages 41--51

    1993

  43. [51]

    , author Li, Z

    author Ling, Z. , author Li, Z. , author Romero, P. , author Han, L. , author Nenadic, G. , year 2025 . title Malei at the plaba track of trec 2024: Roberta for term replacement -- llama3.1 and gpt-4o for complete abstract adaptation . https://arxiv.org/abs/2411.07381, http://...

    2025 arXiv

  44. [52]

    , author Ott, M

    author Liu, Y. , author Ott, M. , author Goyal, N. , author Du, J. , author Joshi, M. , author Chen, D. , author Levy, O. , author Lewis, M. , author Zettlemoyer, L. , author Stoyanov, V. , year 2019 . title Roberta: A robustly optimized bert pretraining approach . journal arX...

    2019 arXiv

  45. [53]

    , author Navigli, R

    author McCarthy, D. , author Navigli, R. , year 2007 . title Semeval-2007 task 10: English lexical substitution task , in: booktitle Proceedings of the fourth international workshop on semantic evaluations (SemEval-2007) , pp. pages 48--53

    2007

  46. [54]

    , author Attal, K

    author Ondov, B. , author Attal, K. , author Demner-Fushman, D. , year 2022 . title A survey of automated methods for biomedical text simplification . journal Journal of the American Medical Informatics Association volume 29 , pages 1976--1988

    2022

  47. [55]

    , author Demner-Fushman, D

    author Ondov, B. , author Demner-Fushman, D. , year 2024 . title Sentence-aligned simplification of biomedical abstracts , in: booktitle International Conference on Artificial Intelligence in Medicine , organization Springer . pp. pages 322--333

    2024

  48. [56]

    , author Wu, J

    author Ouyang, L. , author Wu, J. , author Jiang, X. , author Almeida, D. , author Wainwright, C. , author Mishkin, P. , author Zhang, C. , author Agarwal, S. , author Slama, K. , author Ray, A. , et al., year 2022 . title Training language models to follow instructions with h...

    2022

  49. [57]

    , author Roukos, S

    author Papineni, K. , author Roukos, S. , author Ward, T. , author Zhu, W.J. , year 2002 . title Bleu: a method for automatic evaluation of machine translation , in: booktitle Proceedings of the 40th annual meeting of the Association for Computational Linguistics , pp. pages 311--318

    2002

  50. [58]

    , author Zhang, Y

    author Qi, P. , author Zhang, Y. , author Zhang, Y. , author Bolton, J. , author Manning, C.D. , year 2020 . title Stanza: A python natural language processing toolkit for many human languages , in: booktitle Proceedings of the 58th Annual Meeting of the Association for Comput...

    2020

  51. [59]

    , author Shazeer, N

    author Raffel, C. , author Shazeer, N. , author Roberts, A. , author Lee, K. , author Narang, S. , author Matena, M. , author Zhou, Y. , author Li, W. , author Liu, P.J. , year 2020 . title Exploring the limits of transfer learning with a unified text-to-text transformer . jou...

    2020

  52. [60]

    , author Francis, D.M

    author Rosenberg, S.A. , author Francis, D.M. , author Hullet, C.R. , author Morris, Z.S. , author Brower, J.V. , author Anderson, B.M. , author Bradley, K.A. , author Bassetti, M.F. , author Kimple, R.J. , year 2017 . title Online patient information from radiation oncology d...

    2017

  53. [61]

    , author Huet, S

    author SanJuan, E. , author Huet, S. , author Kamps, J. , author Ermakova, L. , year 2022 . title Overview of the CLEF 2022 simpletext task 1: Passage selection for a simplified summary , in: editor Faggioli, G. , editor Ferro, N. , editor Hanbury, A. , editor Potthast, M. (Ed...

    2022

  54. [62]

    , author Huet, S

    author SanJuan, E. , author Huet, S. , author Kamps, J. , author Ermakova, L. , year 2023 . title Overview of the CLEF 2023 simpletext task 1: Passage selection for a simplified summary , in: editor Aliannejadi, M. , editor Faggioli, G. , editor Ferro, N. , editor Vlachos, M. ...

    2023

  55. [63]

    , author Huet, S

    author SanJuan, E. , author Huet, S. , author Kamps, J. , author Ermakova, L. , year 2024 . title Overview of the clef 2024 simpletext task 1: Retrieve passages to include in a simplified summary , in: booktitle Working Notes of the Conference and Labs of the Evaluation Forum ...

    2024

  56. [64]

    , author Lund, B.D

    author SeyyedHosseini, S. , author Lund, B.D. , author BasirianJahromi, R. , year 2022 . title Comparing health consumer search behavior and scientific research productivity related to covid-19 vaccines in the usa: an infodemiology study . journal Global Knowledge, Memory and ...

    2022

  57. [65]

    , author Jauhar, S.K

    author Specia, L. , author Jauhar, S.K. , author Mihalcea, R. , year 2012 . title Semeval-2012 task 1: English lexical simplification , in: booktitle * SEM 2012: The First Joint Conference on Lexical and Computational Semantics--Volume 1: Proceedings of the main conference and...

    2012

  58. [66]

    , author Mettger, W

    author Stableford, S. , author Mettger, W. , year 2007 . title Plain language: a strategic response to the health literacy challenge . journal Journal of public health policy volume 28 , pages 71--93

    2007

  59. [67]

    , year 2020

    author Stahlberg, F. , year 2020 . title Neural machine translation: A review . journal Journal of Artificial Intelligence Research volume 69 , pages 343--418

    2020

  60. [68]

    , author Pyysalo, S

    author Stenetorp, P. , author Pyysalo, S. , author Topi\' c , G. , author Ohta, T. , author Ananiadou, S. , author Tsujii, J. , year 2012 . title brat : a web-based tool for NLP -assisted text annotation , in: booktitle Proceedings of the Demonstrations Session at EACL 2012 , ...

    2012

  61. [69]

    , author Abend, O

    author Sulem, E. , author Abend, O. , author Rappoport, A. , year 2018 . title Semantic structural evaluation for text simplification , in: booktitle Proceedings of NAACL-HLT , pp. pages 685--696

    2018

  62. [70]

    , year 2020

    author Tatman, R. , year 2020 . title English word frequency . https://www.kaggle.com/datasets/rtatman/english-word-frequency

    2020

  63. [71]

    , author Anil, R

    author Team, G. , author Anil, R. , author Borgeaud, S. , author Alayrac, J.B. , author Yu, J. , author Soricut, R. , author Schalkwyk, J. , author Dai, A.M. , author Hauth, A. , author Millican, K. , et al., year 2023 . title Gemini: a family of highly capable multimodal mode...

    2023 arXiv

  64. [72]

    , author Koehn, P

    author Thompson, B. , author Koehn, P. , year 2019 . title Vecalign: Improved sentence alignment in linear time and space , in: booktitle Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natura...

    2019

  65. [73]

    , author Lavril, T

    author Touvron, H. , author Lavril, T. , author Izacard, G. , author Martinet, X. , author Lachaux, M.A. , author Lacroix, T. , author Rozi \`e re, B. , author Goyal, N. , author Hambro, E. , author Azhar, F. , et al., year 2023 . title Llama: Open and efficient foundation lan...

    2023 arXiv

  66. [74]

    , author Horvitz, E

    author White, R.W. , author Horvitz, E. , year 2009 . title Cyberchondria: studies of the escalation of medical concerns in web search . journal ACM Transactions on Information Systems (TOIS) volume 27 , pages 1--37

    2009

  67. [75]

    , author Debut, L

    author Wolf, T. , author Debut, L. , author Sanh, V. , author Chaumond, J. , author Delangue, C. , author Moi, A. , author Cistac, P. , author Rault, T. , author Louf, R. , author Funtowicz, M. , et al., year 2019 . title Huggingface's transformers: State-of-the-art natural la...

    2019 arXiv

  68. [76]

    , author Unde, I

    author Xia, W. , author Unde, I. , author Ondov, B. , author Demner-Fushman, D. , year 2025 . title Jebs: A fine-grained biomedical lexical simplification task . https://arxiv.org/abs/2506.12898, http://arxiv.org/abs/2506.12898 arXiv:2506.12898

    2025 arXiv

  69. [77]

    , author Napoles, C

    author Xu, W. , author Napoles, C. , author Pavlick, E. , author Chen, Q. , author Callison-Burch, C. , year 2016 . title Optimizing statistical machine translation for text simplification . journal Transactions of the Association for Computational Linguistics volume 4 , pages...

    2016

  70. [78]

    , author Yuan, Z

    author Yuan, H. , author Yuan, Z. , author Gan, R. , author Zhang, J. , author Xie, Y. , author Yu, S. , year 2022 . title Biobart: Pretraining and evaluation of a biomedical generative language model , in: booktitle Proceedings of the 21st Workshop on Biomedical Language Proc...

    2022

  71. [79]

    , author Kishore, V

    author Zhang, T. , author Kishore, V. , author Wu, F. , author Weinberger, K.Q. , author Artzi, Y. , year 2019 . title Bertscore: Evaluating text generation with bert . journal arXiv preprint arXiv:1904.09675

    2019 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.