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REVIEW 4 major objections 7 minor 38 references

GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance

T0 review · 4 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A severity-grouped two-stage pipeline with preference alignment produces better cancer adverse-drug-event summaries than LLM baselines on a new 2,000-post dataset.

desk verdict New cancer-specific ADE summarization corpus and pipeline worth a hard look, but the train/test split and table inconsistencies need fixing before the headline claims hold. read the letter →

arxiv 2505.04284 v1 pith:3DLCFPZV submitted 2025-05-07 cs.CL cs.AI

Sofia Jamil , Aryan Dabad , Bollampalli Areen Reddy , Sriparna Saha , Rajiv Misra , Adil A. Shakur This is my paper · ORCID
classification cs.CLcs.AI
keywords adversedrugeventscancerpharmacovigilanceabstractivesummarizationDirectPreferenceOptimizationQLoRAT5MCADRSdatasetseveritygrouping
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

Cancer patients report side effects online, but existing pharmacovigilance work mostly targets general diseases and ignores reaction severity. This paper proposes to close that gap with a new task, a new dataset, and a new pipeline. The MCADRS dataset contains 2,000 posts from cancer forums, annotated with drug names, adverse drug events, severity and adversity labels, and per-drug gold summaries for 791 cancer drugs. The GASCADE pipeline first extracts drug–event–severity triplets with an LLM fine-tuned through quantized low-rank adapters, groups extracted events by drug and severity with sentence embeddings and hierarchical clustering, and then generates one T5 summary per drug, aligned with Direct Preference Optimization on synthetic preference data. The paper reports that GASCADE outperforms all tested baselines on automated and human metrics, and that removing the first extraction step degrades ROUGE-1 by up to about 70% for T5, so the two-stage design carries the result.

What carries the argument

The load-bearing mechanism is a four-module pipeline. Extraction: a QLoRA-style quantized low-rank adapter fine-tunes an LLM to output drug, adverse event, and severity triplets from noisy patient text. Grouping: sentence embeddings plus hierarchical clustering organize each drug's events into high, moderate, and mild severity tiers. Summarization: T5-Large's encoder–decoder with cross-attention maps the grouped, severity-ordered event set into one abstractive summary per drug. Alignment: Direct Preference Optimization maximizes the log-probability of a preferred summary relative to a dispreferred summary, normalized by a reference model, so the summarizer learns to favor the medically supervised style over machine-generated alternatives.

What would settle it

Have independent clinicians, blinded to model identity, score a random sample of GASCADE summaries against the original posts using the same clinical evaluation, factual recall, and omission rubrics; if the scores do not beat the strongest baseline, or if high-severity events are frequently omitted or downgraded, the superiority claim fails. A complementary check is whether the adverse events named in GASCADE summaries overlap known adverse reactions for each drug in an external structured drug-safety database at better-than-baseline rates.

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Extended reading notes

Core claim

The paper claims that grouped summarization of patient-reported adverse events for cancer drugs is best solved by separating extraction from summarization. Concretely, GASCADE uses an LLM fine-tuned with quantized low-rank adapters to turn each forum post into a (drug, adverse drug event, severity) triplet, clusters those triplets by drug and then by severity tier, and uses T5-Large as an encoder-decoder to write one summary per drug, with the most severe events listed first. It then applies Direct Preference Optimization, using medically supervised summaries as preferred outputs and summaries generated by GPT-4o-mini as dispreferred outputs, to align the summarizer. On the new MCADRS dataset, the paper reports that GASCADE beats all compared baselines on ROUGE, BLEU, BERTScore, METEOR, clinical evaluation score, factual recall, and omission rate, and that the extraction stage is necessary: without it, T5's ROUGE-1 drops by 69.75% and the second-best model's by 32.06%. The conclusion the paper draws is that severity-aware grouping plus preference alignment is what makes the summaries more useful for cancer pharmacovigilance.

Load-bearing premise

The load-bearing premise is that the annotator-written gold summaries in MCADRS are valid ground truth for patient-reported adverse drug events and that the machine-generated dispreferred summaries are valid negative examples, because every quality and alignment result is measured against those two resources.

Editorial extensions

If this is right

  • If the claim holds, clinicians can get per-drug summaries that put high-severity adverse events first, making it easier to anticipate serious side effects during cancer treatment.
  • If the claim holds, the extract-then-summarize design avoids feeding entire forum threads to a single model, sidestepping the context-length failures the paper reports for single-pass LLM baselines.
  • If the claim holds, Direct Preference Optimization on synthetic preference data is a workable low-cost alignment recipe for abstractive medical summarization when human preference pairs are scarce.
  • If the claim holds, MCADRS becomes a public benchmark for future cancer-specific adverse-event extraction and severity-aware summarization.

Reading between the lines

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

  • Editorial inference: because all gold summaries and preference signals come from the same annotation team, the paper's headline result is best read as internal consistency; external clinical review is the natural next step before real-world use.
  • Editorial inference: the extract-group-summarize design should transfer to other disease communities, but the severity tiers would need to be redefined for each condition.
  • Editorial inference: an ablation that removes only the severity sub-grouping, while keeping extraction and drug grouping, would reveal whether severity ordering drives the reported gains.
  • Editorial inference: replacing the machine-generated dispreferred summaries with clinician-corrected summaries would test whether the DPO improvement depends on the synthetic negatives.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper introduces MCADRS, a dataset of 2,000 English posts from cancer-related health forums annotated with drug names, adverse drug events, severity, adversity, and per-drug gold summaries for 791 unique drugs. The authors propose GASCADE, a two-stage pipeline: QLoRA fine-tuned LLM extraction of ADE triplets, severity-based grouping via Sentence-BERT embeddings and hierarchical clustering, T5-Large abstractive summarization of the grouped ADE information, and a Direct Preference Optimization (DPO) alignment stage trained on synthetic preference pairs (annotator-written gold summaries as preferred, GPT-4o-mini outputs as non-preferred). The paper claims GASCADE outperforms all baselines in both automated metrics (ROUGE, BLEU, BERTScore, METEOR) and human evaluations (clinical evaluation score, factual recall, omission rate).

Significance. If the central claims hold, the paper makes a useful contribution by introducing a new task (grouped ADE summarization), a new publicly available dataset, and a reproducible two-stage pipeline that combines extraction, grouping, and DPO-based alignment for encoder-decoder summarization in a domain-specific pharmacovigilance setting. The public release of code and dataset, the inclusion of human evaluation, and the clearly described pipeline are strengths. However, the validity of the reported performance gains depends on the resolution of the evaluation issues raised below, particularly the train/test split granularity and the internal inconsistencies in the reported metrics.

major comments (4)
  1. [Section 5.1 and Section 3] The train/test split is described only as '80% for training, 5% for validation, and 15% for testing,' with no indication that the split is performed at the level of drug groups. Since gold summaries are written per drug (Section 3, 'Writing Gold Summaries') and the task in Section 4 is to generate one summary per drug from grouped posts, a random post-level split will place posts of the same drug in both training and test. In that case, the same drug's gold summary can appear in the DPO/summarization training data and also be used as the test reference, artificially inflating the reported ROUGE/BLEU/BERTScore and possibly the human evaluation scores. The authors must either perform a drug-disjoint split (or at least report results on test drugs never seen in training) and re-run the experiments, or demonstrate quantitatively that no drug overlap exists between train and test.
  2. [Table 3] The reported F1 values for severity prediction are not consistent with the reported precision and recall under the standard formula F1 = 2PR/(P+R). For example, T5 Large FineTuned: P=0.71, R=0.69 would give F1≈0.70, not 0.64; Llama 3 FineTuned: P=0.88, R=0.77 would give F1≈0.82, not 0.79; MedLlama FineTuned: P=0.89, R=0.65 would give F1≈0.75, not 0.57. Please clarify how F1 was computed (e.g., macro vs micro averaging, binary vs multi-class settings) and correct the table; as presented, the extraction module comparisons are unreliable.
  3. [Section 5.3 and Table 2] The text states that 'T5 Large consistently surpasses other models across nearly all metrics and severity levels.' This is contradicted by the BERTScore column in Table 2, where GPT and Llama 3 attain higher BERTScore than T5 Large for every severity level (e.g., High: 0.880 and 0.877 vs 0.831 for T5). Please either correct the claim or restrict it to the specific metrics (e.g., ROUGE and BLEU) where it actually holds.
  4. [Section IV and Section 5.4] The DPO alignment uses 'reference summaries crafted by medical interns' as preferred outputs, while the human and automatic evaluations use the gold-standard annotated summaries as reference. The manuscript does not state whether these are the same summaries; if they are, the DPO improvement (RQ3) is measured against the same gold summaries used to align the model, making the gain in-distribution with respect to the annotation team's judgments. Additionally, none of the tables report error bars or significance tests, so the claimed superiority of GASCADE over the next-best baseline (Table 5, ROUGE-1 0.3926 vs 0.2684) cannot be assessed for statistical reliability. Please clarify the relationship between DPO preferred summaries and the evaluation gold standard, and add significance tests or confidence intervals, e.g., bootstrapping over test instances.
minor comments (7)
  1. [Section 5.4] Human evaluation reports scores from three medical experts on 'randomly selected 20% of the test samples,' but the sample size, inter-annotator agreement, and blinding procedure are not given. Please include these details.
  2. [Tables 2 and 5] Table 2 reports BERTScore values around 0.80-0.88 for T5/GPT/Llama, while Table 5 reports values around 0.25-0.42 for the same models with and without DPO. Please clarify whether these are different evaluation settings (per-severity vs grouped) and why the grouped setting yields much lower BERTScore.
  3. [Table 1] The '%' symbols in Table 1 likely denote 'No,' but they are not defined. Add a legend explaining the symbols and the difference between 'Yes' and '%'.
  4. [Equation (7)] Please clarify the reference policy in the DPO loss: pi_LLM should be the frozen SFT reference model used in DPO. State which model is used as pi_LLM and how its token probabilities are obtained.
  5. [Section IV] The non-preferred summaries are generated by 'GPT-4o mini' but citation [3] is the GPT-4 technical report. Please add the appropriate model card or citation for GPT-4o mini.
  6. [Section 7] The claim that this is the 'first' application of DPO to encoder-decoder models for summarization with synthetic data is strong; please soften it or provide a more thorough related-work search to justify the novelty claim.
  7. [Section 3] The annotation guidelines were developed by medical experts after reviewing '100 samples,' but it is unclear whether these 100 samples are part of the final 2,000-post dataset and whether the annotators were given these examples during annotation. Please clarify.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported GASCADE gains are partially forced by a post-level train/test split: per-drug gold summaries used as training targets recur as test references, inflating the Table 5 margins.

  1. fitted input called prediction [Section 3 (Writing Gold Summaries), Section 4 (Problem Statement), Section 5.1 (Experimental Setup)]
    ""annotators crafted summaries for each drug name in accordance with medical team's guidelines" (Sec. 3); "For each drug cluster G_j, abstractive summarization is performed, generating summaries S_j ... each S_j summarizing the ADRs for drug j" (Sec. 4); "The dataset was split into 80% for training, 5% for validation, and 15% for testing" (Sec. 5.1)."

    The evaluation unit is the drug: one gold summary is written for each drug from all of that drug's posts, and the task is to generate one summary per drug cluster. The train/test split is described only as an 80/5/15 split of the dataset, with no drug-disjoint construction. With 2,000 posts and 791 unique drugs, drugs that appear in both training and test have their single gold summary present as a training target and as the test reference. The model is therefore fitted to the exact summaries it is later asked to 'predict' for those drugs, so the Table 5 margins (e.g., GASCADE After DPO Rouge-1 0.3926 vs. next best 0.2684) are partly memorization rather than independent generalization.

full rationale

This is an empirical systems paper with no equation-level derivation to reduce; the architecture (QLoRA extraction, Sentence-BERT grouping, T5 summarization, DPO) is a standard pipeline and each component is evaluated on a held-out split. I find no load-bearing self-citation: the cited works with overlapping authors are related-work or metric-attribution citations, and the extraction results in Table 3 are self-contained. The central circularity is in the evaluation construction. Section 3 builds one gold summary per drug from all posts for that drug; Section 4 defines the task as one summary per drug cluster; Section 5.1 gives only an 80/5/15 post-level split. Any drug present in both training and test therefore has its gold summary as a training target and test reference, so the summarization 'predictions' for those drugs reduce to fitted targets. The reported automated gains in Table 5 are inflated by this overlap, and the human evaluation (which compares to the same gold summaries) inherits the issue. The DPO stage's preferred summaries are human reference summaries; if they coincide with the gold summaries, the DPO gain is also aligned with the test reference, but the text does not explicitly state their identity, so the definite circular step is the non-drug-disjoint split. Score 6: partial circularity, because the extraction module and any truly unseen drugs retain independent content.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims depend mainly on the validity of the annotation process and the synthetic preference data, not on a formal derivation. Unreported hyperparameters and clustering thresholds add hidden degrees of freedom that affect reproducibility.

free parameters (4)
  • DPO scaling factor beta = not reported
    Equation 7 includes beta as a free scale controlling preference sharpness; the manuscript does not report its value or tuning procedure in Section 4.IV.
  • QLoRA rank and alpha = not reported
    Section 4.I invokes QLoRA adapters without reporting rank, alpha, or quantization settings, which directly affect extraction quality.
  • Clustering cutoff for severity grouping = not reported
    Section 4.II uses hierarchical clustering to assign severity groups but does not specify distance threshold, linkage, or number of clusters.
  • Fine-tuning and decoding hyperparameters = not reported
    Section 5.1 reports GPU and runtime but not learning rates, batch sizes, epochs, or decoding parameters for T5 and baselines.
assumptions (5)
  • domain assumption Annotator-written gold summaries are an accurate ground truth for ADE summarization.
    Invoked throughout Sections 3 and 5; both DPO preferences and evaluation rely on these summaries without external clinical validation.
  • ad hoc to paper GPT-4o-mini generated summaries are valid non-preferred outputs for DPO.
    Introduced in Section 4.IV; no analysis shows these outputs are actually less preferred or medically safe.
  • domain assumption Sentence-BERT embeddings and hierarchical clustering group ADEs into meaningful severity classes.
    Invoked in Section 4.II; grouping quality is not evaluated against severity labels.
  • domain assumption Posts from Cancer Research UK and Cancer Survival Network are representative of cancer patients' ADE experiences.
    Data collection in Section 3 relies on these two forums without representativeness analysis.
  • domain assumption ROUGE, BLEU, BERTScore, and METEOR are appropriate proxies for summary quality in a medical setting.
    Used in Section 5.3; correlation with clinical usefulness is not established, though human evaluation is also included.

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Cite this review

Pith. "Pith review of GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance." pith.science (2026). https://pith.science/paper/3DLCFPZV

@misc{pith2026250504284,
  author       = {Pith},
  title        = {Pith review of: GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3DLCFPZV}},
  note         = {Machine review of arXiv:2505.04284}
}
read the original abstract

In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While the volume and complexity of pharmacovigilance data have increased, existing research in this field has predominantly focused on general diseases rather than specifically addressing cancer. This work introduces the task of grouped summarization of adverse drug events reported by multiple patients using the same drug for cancer treatment. To address the challenge of limited resources in cancer pharmacovigilance, we present the MultiLabeled Cancer Adverse Drug Reaction and Summarization (MCADRS) dataset. This dataset includes pharmacovigilance posts detailing patient concerns regarding drug efficacy and adverse effects, along with extracted labels for drug names, adverse drug events, severity, and adversity of reactions, as well as summaries of ADEs for each drug. Additionally, we propose the Grouping and Abstractive Summarization of Cancer Adverse Drug events (GASCADE) framework, a novel pipeline that combines the information extraction capabilities of Large Language Models (LLMs) with the summarization power of the encoder-decoder T5 model. Our work is the first to apply alignment techniques, including advanced algorithms like Direct Preference Optimization, to encoder-decoder models using synthetic datasets for summarization tasks. Through extensive experiments, we demonstrate the superior performance of GASCADE across various metrics, validated through both automated assessments and human evaluations. This multitasking approach enhances drug-related decision-making and fosters a deeper understanding of patient concerns, paving the way for advancements in personalized and responsive cancer care. The code and dataset used in this work are publicly available.

Figures

Figures reproduced from arXiv: 2505.04284 by the authors.

Figure 1
Figure 1. Our GASCADE Framework summarising the Adverse Drug Event Information for each drug This motivated our study on the task of summarizing ADE related to cancer medi￾cations. We propose a novel two-phase approach for extracting and summarizing ADRs, introducing a dataset curated from online cancer forums where patients discuss drugs used in cancer therapy and their experiences. Our work represents the first attempt to i… view at source ↗
Figure 2
Figure 2. Proposed Model Architecture of Grouping and Abstractive Summarization of Cancer Ad￾verse Drug events (GASCADE). I. ADE Extraction Module: To extract ADE related information, we designed an in￾formation extraction pipeline. We formulated the task as follows: Given a dataset of medical instances {(𝑃𝑖 , 𝐷𝑖 , 𝐸𝑖 )}𝑁 𝑖=1, where 𝑃𝑖 represents the user post, 𝐷𝑖 denotes the associated drug name(s), 𝐸𝑖 is the adverse drug ev… view at source ↗
Figure 3
Figure 3. Samples summaries generated with and without alignment techniques Models Rouge 1 Rouge 2 Rouge L Bleu 1 Bleu 2 Bleu 3 Bert Score Meteor T5 Large 0.0900 0.0105 0.0738 0.0464 0.0094 0.0027 0.1251 0.0799 Phi3 0.1512 0.0267 0.1333 0.0925 0.0329 0.0093 0.2393 0.1608 Gemma 0.0677 0.0096 0.0578 0.0308 0.0095 0.0019 0.0867 0.0618 Mistral 0.1188 0.0204 0.1044 0.0631 0.0209 0.0048 0.1564 0.1081 Medalpaca 0.1217 0.0215 0.1027 … view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Samples responses generated via our framework and other models [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

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Reference graph

Works this paper leans on

38 extracted references · 30 canonical work pages

  1. [1]

    A. B. Abacha, W. wai Yim, G. Michalopoulos, and T. Lin. An investigation of evaluation metrics for automated medical note generation, 2023

    work page 2023

  2. [2]

    Abdin, J

    M. Abdin, J. Aneja, H. Awadalla, A. Awadallah, A. A. Awan, N. Bach, A. Bahree, A.Bakhtiari,J.Bao,H.Behl,A.Benhaim,M.Bilenko,J.Bjorck,S.Bubeck,M.Cai,Q.Cai, V. Chaudhary, D. Chen, D. Chen, W. Chen, Y.-C. Chen, Y.-L. Chen, H. Cheng, P. Chopra, X. Dai, M. Dixon, R. Eldan, V. Fragoso, J. Gao, M. Gao, M. Gao, A. Garg, A. D. Giorno, A. Goswami, S. Gunasekar, E. ...

    work page 2024

  3. [3]

    Achiam, S

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  4. [4]

    Llama 3 model card

    AI@Meta. Llama 3 model card. 2024

    2024

  5. [5]

    Banerjee and A

    S. Banerjee and A. Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. InProceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65–72, 2005

    2005

  6. [6]

    when ‘bad’is ‘good’

    R.Daniulaityte,L.Chen,F.R.Lamy,R.G.Carlson,K.Thirunarayan,A.Sheth,etal. “when ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets. JMIR public health and surveillance, 2(2):e6327, 2016

    work page 2016

  7. [7]

    Dettmers, A

    T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer. Qlora: Efficient finetuning of quantized llms.Advances in Neural Information Processing Systems, 36, 2024

    work page 2024

  8. [8]

    for Disease Control, Prevention, et al

    C. for Disease Control, Prevention, et al. Adverse drug events in adults.CDC. Retrieved November, 19:2022, 2017

    work page 2022

Show all 38 references
  1. [9]

    Ghosh, A

    A. Ghosh, A. Acharya, R. Jain, S. Saha, A. Chadha, and S. Sinha. Clipsyntel: clip and llm synergy for multimodal question summarization in healthcare. InProceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 22031–22039, 2024

    2024

  2. [10]

    Ghosh, A

    A. Ghosh, A. Acharya, P. Jha, S. Saha, A. Gaudgaul, R. Majumdar, A. Chadha, R. Jain, S. Sinha, and S. Agarwal. Medsumm: A multimodal approach to summarizing code-mixed hindi-englishclinicalqueries. In EuropeanConferenceonInformationRetrieval ,pages106–

  3. [11]

    Ghosh, M

    A. Ghosh, M. Tomar, A. Tiwari, S. Saha, J. Salve, and S. Sinha. From sights to insights: To- wards summarization of multimodal clinical documents. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13117–13129, 2024

    2024

  4. [12]

    Gurulingappa, A

    H. Gurulingappa, A. M. Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, and L. Toldo. Development of a benchmark corpus to support the automatic extraction of drug-related ad- verse effects from medical case reports.Journal of biomedical informatics, 45(5):885–892, 2012

    2012

  5. [13]

    Medalpaca–anopen-sourcecollectionofmedicalconversationalaimod- els and training data.arXiv preprint arXiv:2304.08247, 2023

    T.Han,L.C.Adams,J.-M.Papaioannou,P.Grundmann,T.Oberhauser,A.Löser,D.Truhn, andK.K.Bressem. Medalpaca–anopen-sourcecollectionofmedicalconversationalaimod- els and training data.arXiv preprint arXiv:2304.08247, 2023

    2023 arXiv

  6. [14]

    Y. Hou, X. Li, G. Wu, and X. Ye. National adr monitoring system in china.Drug safety, 39:1043–1051, 2016

    2016

  7. [15]

    S.Hussain,H.Afzal,R.Saeed,N.Iltaf,andM.Y.Umair. Pharmacovigilancewithtransform- ers: A framework to detect adverse drug reactions using bert fine-tuned with farm.Compu- tational and Mathematical Methods in Medicine, 2021(1):5589829, 2021

    2021

  8. [16]

    Lavril, T

    A.Q.Jiang,A.Sablayrolles,A.Mensch,C.Bamford,D.S.Chaplot,D.delasCasas,F.Bres- sand,G.Lengyel,G.Lample,L.Saulnier,L.R.Lavaud,M.-A.Lachaux,P.Stock,T.L.Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed. Mistral 7b, 2023

    2023

  9. [17]

    Joshi, D

    M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy. Spanbert: Improving pre-training by representing and predicting spans.Transactions of the association for com- putational linguistics, 8:64–77, 2020. Title Suppressed Due to Excessive Length 15

    2020

  10. [18]

    Karimi, C

    S. Karimi, C. Wang, A. Metke-Jimenez, R. Gaire, and C. Paris. Text and data mining tech- niques in adverse drug reaction detection.ACM Computing Surveys (CSUR), 47(4):1–39, 2015

    2015

  11. [19]

    J. Li, Y. Sun, R. J. Johnson, D. Sciaky, C.-H. Wei, R. Leaman, A. P. Davis, C. J. Mattingly, T. C. Wiegers, and Z. Lu. Biocreative v cdr task corpus: a resource for chemical disease relation extraction.Database, 2016, 2016

    2016

  12. [20]

    Z. Li, Z. Yang, L. Luo, Y. Xiang, and H. Lin. Exploiting adversarial transfer learning for adverse drug reaction detection from texts.Journal of biomedical informatics, 106:103431, 2020

    2020

  13. [21]

    C.-Y. Lin. Rouge: A package for automatic evaluation of summaries. InText summarization branches out, pages 74–81, 2004

    2004

  14. [22]

    F. Liu, A. Jagannatha, and H. Yu. Towards drug safety surveillance and pharmacovigi- lance:currentprogressindetectingmedicationandadversedrugeventsfromelectronichealth records. Drug safety, 42(1):95–97, 2019

    2019

  15. [23]

    Mohammadi, F

    Y. Mohammadi, F. Ghasemian, J. Varshosaz, and M. Sattari. Classifying referring/non- referring adr in biomedical text using deep learning.Informatics in Medicine Unlocked, 39:101246, 2023

    2023

  16. [24]

    Nikfarjam, A

    A. Nikfarjam, A. Sarker, K. O’connor, R. Ginn, and G. Gonzalez. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3):671–681, 2015

    2015

  17. [25]

    Oronoz, K

    M. Oronoz, K. Gojenola, A. Pérez, A. D. De Ilarraza, and A. Casillas. On the creation of a clinical gold standard corpus in spanish: Mining adverse drug reactions.Journal of biomed- ical informatics, 56:318–332, 2015

    2015

  18. [26]

    Papineni, S

    K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu. Bleu: a method for automatic evaluation of machine translation. InProceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318, 2002

    2002

  19. [27]

    Patki, A

    A. Patki, A. Sarker, P. Pimpalkhute, A. Nikfarjam, R. Ginn, K. O’Connor, K. Smith, and G. Gonzalez. Mining adverse drug reaction signals from social media: going beyond extrac- tion. Proceedings of BioLinkSig, 2014:1–8, 2014

    2014

  20. [28]

    Rafailov, A

    R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn. Direct pref- erence optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36, 2024

    2024

  21. [29]

    Raffel, N

    C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

    2020

  22. [30]

    N. Reimers. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084, 2019

    1908 arXiv

  23. [31]

    Sahoo, P

    P. Sahoo, P. Meharia, A. Ghosh, S. Saha, V. Jain, and A. Chadha. Unveiling hallucination intext,image,video,andaudiofoundationmodels:Acomprehensivesurvey. arXivpreprint arXiv:2405.09589, 2024

    2024 arXiv

  24. [33]

    Sarker and G

    A. Sarker and G. Gonzalez. Portable automatic text classification for adverse drug reaction detection via multi-corpus training.Journal of biomedical informatics, 53:196–207, 2015

    2015

  25. [34]

    S.Shareef,C.Naidu,S.R.Raikar,Y.V.Rao,andU.Devika. Development,implementation, and analysis of adverse drug reaction monitoring system in a rural tertiary care teaching hospital in narketpally, telangana.Int J Basic Clin Pharmacol, 4(4):757–60, 2015

    2015

  26. [35]

    Adversedrugre- actionsatadversedrugreactionmonitoringcenterinraipur:Analysisofspontaneousreports during 1 year.Indian journal of pharmacology, 49(6):432–437, 2017

    P.Singh,M.Agrawal,R.Hishikar,U.Joshi,B.Maheshwari,andA.Halwai. Adversedrugre- actionsatadversedrugreactionmonitoringcenterinraipur:Analysisofspontaneousreports during 1 year.Indian journal of pharmacology, 49(6):432–437, 2017. 16 S. Jamil et al

    2017

  27. [36]

    G. Team. Gemma. 2024

    2024

  28. [37]

    Tunstall, E

    L. Tunstall, E. Beeching, N. Lambert, N. Rajani, K. Rasul, Y. Belkada, S. Huang, L. von Werra, C. Fourrier, N. Habib, N. Sarrazin, O. Sanseviero, A. M. Rush, and T. Wolf. Zephyr: Direct distillation of lm alignment, 2023

    2023

  29. [38]

    A. J. Viera, J. M. Garrett, et al. Understanding interobserver agreement: the kappa statistic. Fam med, 37(5):360–363, 2005

    2005

  30. [39]

    Zhang, V

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

    1904 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.