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Journal articles on the topic 'Multi-label prompting'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Victor, Livernoche, and Sujaya Vidya. "[Re] A Reproduction of Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification." ReScience C 9, no. 2 (2023): #33. https://doi.org/10.5281/zenodo.8173735.

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2

Doropoulos, Stavros, Elisavet Karapalidou, Polychronis Charitidis, Sophia Karakeva, and Stavros Vologiannidis. "Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis." Applied Sciences 15, no. 14 (2025): 8059. https://doi.org/10.3390/app15148059.

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The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed un

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3

Masri, Yahya, Zifu Wang, Anusha Srirenganathan Malarvizhi, et al. "Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example." Algorithms 18, no. 7 (2025): 420. https://doi.org/10.3390/a18070420.

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To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG

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4

Garaus, Marion, Christian Garaus, Elisabeth Wolfsteiner, and Charlotte Jermendy. "Anthropomorphism as a Differentiation Strategy for Standardized Reusable Glass Containers." Sustainability 14, no. 15 (2022): 9473. http://dx.doi.org/10.3390/su14159473.

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The steadily increasing amount of waste requires new strategies for package waste reduction. One strategy is to switch from single-use plastic packaging to glass packaging; however, this strategy is only beneficial from an environmental perspective when complemented with a multi-use deposit refund system with standardized glass containers. This implies the loss of package shape as a differentiation criterion, which has been considered a highly relevant marketing instrument in the fast-moving consumer goods markets. Against this background, the current research investigates in an online experim

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5

Yu, En, Songtao Liu, Zhuoling Li, et al. "Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3304–12. http://dx.doi.org/10.1609/aaai.v37i3.25437.

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Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domain. The domain generalization problem of MOT is hardly studied. To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. However, it is infeasible to label every

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6

Lei, Zhou, Yawei Zhang, and Shengbo Chen. "A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis." Applied Sciences 14, no. 19 (2024): 8719. http://dx.doi.org/10.3390/app14198719.

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Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does not realize the information interaction in the generation process, and it ignores the dependency between the prompt template and the aspect terms and opinion terms in the input sequence. In this paper, we propose a Dual-template Prompted Mutual Learning (DPML) generative model to enhance the inform

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7

Slotkin, Emily K., Chelsey Burke, Meera Hameed, et al. "Off-label use of fam-trastuzumab deruxtecan-nxki with early activity in a cohort of patients with desmoplastic small round cell tumor." Journal of Clinical Oncology 42, no. 16_suppl (2024): 11584. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.11584.

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11584 Background: Desmoplastic small round cell tumor (DSRCT) is a rare and aggressive sarcoma occurring predominantly in pediatric, adolescent and young adult biologic males, and is molecularly characterized by the pathognomonic EWSR1-WT1 fusion. Despite aggressive multi-modality therapy outcomes for those with DSRCT remain poor, and more effective therapies are urgently required. Prior pre-clinical data and transcriptomic analyses of DSRCT patients has suggested the relevance of the ERBB2 pathway in this disease, prompting the off-label use of fam-trastuzumab deruxtecan-nxki (T-DXd) in the t

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8

Kumar, Anupama Deepa, Michelle Padilla, Lin Liu, et al. "Phase II study of the combination of daratumumab, ixazomib, pomalidomide, and dexamethasone as salvage therapy in relapsed/refractory multiple myeloma: Stage 2 interim results." Journal of Clinical Oncology 40, no. 16_suppl (2022): 8041. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.8041.

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8041 Background: The combination of daratumumab (Dara), pomalidomide (POM), and dexamethasone (dex) (DPd) has previously demonstrated deep and durable responses including high rates of minimal residual disease (MRD) negativity, in patients with relapsed/refractory (R/R) MM. Quadruplet regimens may further improve results. We report updated findings from a phase 2 multicenter trial of the addition of ixazomib to DPd (DIPd) in patients with early R/R MM. Methods: This is a prospective, multi-center, open-label, single arm phase II trial with a primary objective to evaluate the overall response r

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9

Halpern, Anna B., Elihu H. Estey, Megan Othus, et al. "Mitoxantrone, Etoposide, and Cytarabine (MEC) Following Epigenetic Priming with Decitabine in Adults with Relapsed/Refractory Acute Myeloid Leukemia (AML) or High-Risk Myelodysplastic Syndrome (MDS): A Phase 1 Study." Blood 124, no. 21 (2014): 3730. http://dx.doi.org/10.1182/blood.v124.21.3730.3730.

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Abstract Background: Most adults with AML or high-risk MDS will ultimately require salvage therapy. However, the likelihood of achieving a complete remission (CR) with standard salvage regimens is often 15-20% or less. Emerging data suggest that pre-treatment (“priming”) with a hypomethylating agent such as decitabine can sensitize AML cells to chemotherapeutics, prompting a dose-escalation study of MEC preceded by decitabine-priming in relapsed/refractory AML and high-risk MDS. Methods: This single arm study aimed to estimate the maximum tolerated dose (MTD) of decitabine-primed MEC in adults

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10

Kumar, Anupama, Aaron S. Rosenberg, Edward D. Ball, et al. "Phase II Study of the Combination of Daratumumab, Ixazomib, Pomalidomide, and Dexamethasone (DIPD) As Salvage Therapy in Early Relapsed/Refractory Multiple Myeloma." Blood 142, Supplement 1 (2023): 4750. http://dx.doi.org/10.1182/blood-2023-181091.

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Background: The combination of daratumumab (D), pomalidomide (P), and dexamethasone (d) has previously demonstrated high overall response rates in patients with RRMM. Results of the phase 3 APOLLO study of DPd vs Pd in patients with RRMM with at least 1 prior line of therapy showed 69% ORR with a median PFS of 12.4 months in those patients who received the triplet combination (Dimopoulos, et al. Lancet 2021). Quadruplet regimens may further improve results by providing deeper and more durable responses with more intensive regimens. We report results from a phase 2 multicenter trial of the addi

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11

Kasner, Margaret, Selina M. Luger, Grace R. Jeschke, Rosemarie Mick, Martin Carroll, and Alexander E. Perl. "Single-Cell Pharmacodynamic Monitoring of S6 Ribosomal Protein in AML Blasts During Trials Combining Sirolimus and Intensive Chemotherapy: Target Inhibition Enhances Response." Blood 118, no. 21 (2011): 230. http://dx.doi.org/10.1182/blood.v118.21.230.230.

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Abstract Abstract 230 Background: Mammalian target of rapamycin (mTOR) inhibitors enhance cytotoxic chemotherapy effects in primary acute leukemia samples in preclinical assays, prompting multi-center evaluation of regimens combining mTOR inhibitors plus induction chemotherapy in AML. As mTOR is frequently but not uniformly activated in primary AML samples, it is unclear which patients benefit from this targeted approach. Thus, we sought to monitor mTOR kinase activity during therapy to determine whether target activation and/or inhibition predicted clinical response. We previously reported th

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12

Le, Trang, Casper F. Winsnes, Ulrika Axelsson, et al. "Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition." Nature Methods, September 29, 2022. http://dx.doi.org/10.1038/s41592-022-01606-z.

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AbstractWhile spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competi

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13

Li, Jiaao, Yixiang Huang, Ming Wu, Bin Zhang, Xu Ji, and Chuang Zhang. "CLIP-SP: Vision-language model with adaptive prompting for scene parsing." Computational Visual Media, August 27, 2024. http://dx.doi.org/10.1007/s41095-024-0430-4.

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AbstractWe present a novel framework, CLIP-SP, and a novel adaptive prompt method to leverage pre-trained knowledge from CLIP for scene parsing. Our approach addresses the limitations of DenseCLIP, which demonstrates the superior image segmentation provided by CLIP pre-trained models over ImageNet pre-trained models, but struggles with rough pixel-text score maps for complex scene parsing. We argue that, as they contain all textual information in a dataset, the pixel-text score maps, i.e., dense prompts, are inevitably mixed with noise. To overcome this challenge, we propose a two-step method.

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14

Parker, Michael J., Caitlin Anderson, Claire Stone, and YeaRim Oh. "A Large Language Model Approach to Educational Survey Feedback Analysis." International Journal of Artificial Intelligence in Education, June 25, 2024. http://dx.doi.org/10.1007/s40593-024-00414-0.

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AbstractThis paper assesses the potential for the large language models (LLMs) GPT-4 and GPT-3.5 to aid in deriving insight from education feedback surveys. Exploration of LLM use cases in education has focused on teaching and learning, with less exploration of capabilities in education feedback analysis. Survey analysis in education involves goals such as finding gaps in curricula or evaluating teachers, often requiring time-consuming manual processing of textual responses. LLMs have the potential to provide a flexible means of achieving these goals without specialized machine learning models

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15

Chen, Peng, Jian Wang, Ling Luo, Hongfei Lin, and Zhihao Yang. "Learning to Explain is a Good Biomedical Few-Shot Learner." Bioinformatics, October 3, 2024. http://dx.doi.org/10.1093/bioinformatics/btae589.

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Abstract Motivation Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g., rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedi

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16

Li, Lin, Namrata Srivastava, Jia Rong, Quanlong Guan, Dragan Gašević, and Guanliang Chen. "When and how biases seep in: Enhancing debiasing approaches for fair educational predictive analytics." British Journal of Educational Technology, March 24, 2025. https://doi.org/10.1111/bjet.13575.

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AbstractThe use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling pipeline, numerous debiasing approaches have been proposed. Yet, research into state‐of‐the‐art techniques for effectively employing these approaches to enhance fairness in educational predictive scenarios remains limited. Prior studies often focused

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17

Holden, Todd Joseph Miles. "The Evolution of Desire in Advertising." M/C Journal 2, no. 5 (1999). http://dx.doi.org/10.5204/mcj.1773.

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She's the dollars, she's my protection; she's a promise, in the year of election. Sister, I can't let you go; I'm like a preacher, stealing hearts at a traveling show. For love or money, money, money... Desire -- U2, "Desire" (1988) For the love of money. In the worship of things. Desire has traditionally been employed by advertising as a means of selling product. Regardless of culture, more powerful than context, desire is invoked as one of capitalism's iron-clad codes of quality. The Uses of Desire in Advertising Specifically, two variants have been most common. That in which desire is: (1)

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