├── README.md └── paper.pdf /README.md: -------------------------------------------------------------------------------- 1 | # Visual Analytics Research for Improving Training Data Quality 2 | ♠ (citations > 200) 3 | 4 | * Max-margin majority voting for learning from crowds (NIPS 2015) [pdf]( https://proceedings.neurips.cc/paper/2015/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf ) 5 | * Improving learning-from-crowds through expert validation (IJCAI 2017) [pdf]( http://ml.cs.tsinghua.edu.cn/~jun/pub/expert-validation-ijcai2017.pdf ) 6 | * ImageNet: A large-scale hierarchical image database (CVPR 2009) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5206848 ) ♠ 7 | * Limits on learning machine accuracy imposed by data quality (NIPS 1995) [pdf]( https://proceedings.neurips.cc/paper/1994/file/1e056d2b0ebd5c878c550da6ac5d3724-Paper.pdf ) 8 | * 大数据可用性的研究进展 (软件学报 2016) [html]( http://www.jos.org.cn/html/2016/7/5038.htm ) 9 | * To err is human: Building a safer health system (NAP 2000) [pdf]( https://omsorgsforskning.brage.unit.no/omsorgsforskning-xmlui/bitstream/handle/11250/2445271/Kohn.pdf?sequence=1 ) ♠ 10 | * Pervasive label errors in test sets destabilize machine learning benchmarks (NeurIPS Datasets and Benchmarks 2021) [pdf](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/f2217062e9a397a1dca429e7d70bc6ca-Paper-round1.pdf) ♠ 11 | * Data warehousing special report: Data quality and the bottom line (ADT 2002) [pdf]( http://www.estgv.ipv.pt/PaginasPessoais/jloureiro/ESI_AID2007_2008/fichas/TP06_anexo1.pdf ) 12 | * Data management: Still a major obstacle to AI success (2019) [html]( https://www.datanami.com/2019/05/22/data-management-still-a-major-obstacle-to-ai-success/) 13 | * Towards better analysis of machine learning models: A visual analytics perspective (Visual Informatics 2017) [html]( https://www.sciencedirect.com/science/article/pii/S2468502X17300086?ref=pdf_download&fr=RR-2&rr=70b7a3563a5c8b51 ) ♠ 14 | * Visual analytics in deep learning: An interrogative survey for the next frontiers (TVCG 2019) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8371286 ) ♠ 15 | * VIS4ML: An ontology for visual analytics assisted machine learning (TVCG 2019) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8440124 ) 16 | * Recent research advances on interactive machine learning (JOV 2019) [html]( https://link.springer.com/article/10.1007/s12650-018-0531-1 ) 17 | * The state of the art in enhancing trust in machine learning models with the use of visualizations (CGF 2020) [pdf](https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.14034) 18 | * State of the art of visual analytics for explainable deep learning (CGF 2023) [pdf](https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14733) 19 | * “Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI (CHI 2021) [pdf](https://dl.acm.org/doi/pdf/10.1145/3411764.3445518) ♠ 20 | * Towards human-guided machine learning (IUI 2019) [pdf](https://dl.acm.org/doi/pdf/10.1145/3301275.3302324) 21 | * A survey of visual analytics techniques for machine learning (CVM 2021) [pdf]( https://link.springer.com/content/pdf/10.1007/s41095-020-0191-7.pdf ) 22 | * 可视化与人工智能交叉研究综述 (中国科学 2021) [pdf]( http://scis.scichina.com/cn/2021/SSI-2021-0062.pdf ) 23 | * Extending the nested model for user-centric XAI: A design study on GNN-based drug repurposing (TVCG 2023) [pdf](https://ieeexplore.ieee.org/iel7/2945/4359476/09916585.pdf) 24 | * Onelabeler: A flexible system for building data labeling tools (CHI 2022) [pdf](https://dl.acm.org/doi/pdf/10.1145/3491102.3517612) 25 | * Symphony: Composing interactive interfaces for machine learning (CHI 2022) [pdf](https://dl.acm.org/doi/pdf/10.1145/3491102.3502102) 26 | * Steering data quality with visual analytics: The complexity challenge (Visual Informatics 2018) [pdf]( https://pdf.sciencedirectassets.com/315710/1-s2.0-S2468502X19X00029/1-s2.0-S2468502X18300573/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjED0aCXVzLWVhc3QtMSJGMEQCIDjPfRSZgfGb7vzU0Y9WhXfsI%2BmdtvrR1%2Bvxtwvg4qV1AiArmjxgSV%2BuYiXMueAMO48EgVfdX7FYGL3SMNuirCuqWirSBAgWEAQaDDA1OTAwMzU0Njg2NSIMEzpAEmGBHqmH0O%2FBKq8E%2F%2F%2BacDf0SImQkCQ7flpAmcQgXBgav5G4GdPn4rmy7L4oL%2BFTruu9274qnRzU3K6x9XKqnJtRgLUsXvQxNkE%2FIaXLOOKKKbaPQxcqGhz2XcxR8wTYO9FmEQfctx5kq0Ukb3vqc7H5o6u%2FPQp84yvezAhDVowvUBwcIjUaRMOp3uE2C1Ghk5NIIdJW6bpYG7UuFChOlwi88zGGWzdcEjePw%2FoGam05V59DncDpFAsGNSTxko6U4TGl44puHklzN7UhbMUOJrMhb9eCmGaSVY6cHFx9RW7hEkApGjR1B%2BiA8EgTkqDXFozgW%2BWxeeuPu2SvE6S1eCLrseGu2A1b0TtS%2BPH3mnRwVhMw%2BXDyYRz6l%2B4x8XkvhLhNUV0BbHSw%2F4IagL8ZeBf%2FmBoll6DkpmNaHSRkov7yE%2FUaKitluDkMmvQoe3j0xbFaLSX%2FeBicpUuuYld1Ky4BwCzeilxgf%2BPE70iQLCbXSeA9QqdcZb%2FdO%2B3vofKTP3uOzgjGd4Ht4FfDdWhowJpp%2B%2Bofvh0QrIE6FWVje1vK5RVMKh9aHKS6uYIwGgbUBSNGwJ2PCDsHjJykYzzQi4JsJCA3tkMsg7P4Q5OcYpICgEDBnwLmuGT4mO8QhqU98fkAZLjc3U7Lq8EmbT8pI6EKWAyqnzIGehKjjch73fpWongPSIZPC9AWUIzVqGlaTm%2FeB87zai0P93ZiPkePxL6X49bktPQkyP%2BEsjlC08cwAnjT0GT5zDw8oTCswOmTBjqqAWkbwbiT%2BRnJ3i%2BzCdgD3cU6rvNSevievlV0muinPaEFWS%2F1TDZIAy4GvXwZnTrb%2FKKZZAaPeYrD66qfGI5SzeklPuX1nUW66ynIRxRaAB58RecEAyXkx87S%2BtxnS%2FHlVVAaIEeiSHC1ht7VN5jJQh2BATEChCJFwBlXGY4x9ociyYQ8ZECO0j5LeoBfsj%2FRjZfGYifcg5SOeH9mHuvkIDIoiK4cxXoWpGVk&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220510T133747Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYYQK3GESR%2F20220510%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2ff5b3fe75d00dc99d4a384be343b72a294ac6cb10d0544782f240e1ca06a531&hash=362883a7cbb9e98cfa627b4a920012eeca6f5e2f42c21e74233b27377d290d1b&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2468502X18300573&tid=spdf-9cfe7c1c-293a-49d2-b994-3ac2e72f84bd&sid=7238f6fe46c4094db00b9548037327939cf7gxrqa&type=client&ua=4d52595d5456500b5006&rr=7093124e7af46e64) 27 | * C2A: Crowd consensus analytics for virtual colonoscopy (VAST 2016) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7883508 ) 28 | * CMed: Crowd analytics for medical imaging data (TVCG 2021) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8907502 ) 29 | * An interactive method to improve crowdsourced annotations (TVCG 2019) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8440116 ) 30 | * Interactive correction of mislabeled training data (VAST 2019) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8986943 ) 31 | * An approach to supporting incremental visual data classification (TVCG 2015) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6840370 ) 32 | * V‐Awake: A visual analytics approach for correcting sleep predictions from deep learning models (CGF 2019) [pdf](https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13667) 33 | * Classifier‐guided visual correction of noisy labels for image classification tasks (CGF 2020) [pdf]( https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13973 ) 34 | * DETOXER: A visual debugging tool with multiscope explanations for temporal multilabel classification (CG&A 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9866547) 35 | * LabelVizier: Interactive validation and relabeling for technical text annotations (arXiv 2023) [pdf](https://arxiv.org/pdf/2303.17820) 36 | * VANT: A visual analytics system for refining parallel corpora in neural machine translation (PacificVis 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9787874) 37 | * OoDAnalyzer: Interactive analysis of out-of-distribution samples (TVCG 2021) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8994105 ) 38 | * DeepLens: Interactive out-of-distribution data detection in NLP models (CHI 2023) [pdf]( https://dl.acm.org/doi/pdf/10.1145/3544548.3580741) 39 | * Contrastive identification of covariate shift in image data (VIS 2021) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9623289) 40 | * Perspective: Leveraging human understanding for identifying and characterizing image atypicality (IUI 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3581641.3584096) 41 | * HetVis: A visual analysis approach for identifying data heterogeneity in horizontal federated learning (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9912364) 42 | * DataPilot: Utilizing quality and usage information for subset selection during visual data preparation (CHI 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3544548.3581509) 43 | * Analyzing the noise robustness of deep neural networks (VAST 2018) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8802509) 44 | * Analyzing the noise robustness of deep neural networks (TVCG 2021) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8967166) 45 | * VERB: Visualizing and interpreting bias mitigation techniques for word representations (arXiv 2021) [pdf](https://arxiv.org/pdf/2104.02797) 46 | * Visual identification of problematic bias in large label spaces (arXiv 2022) [pdf](https://arxiv.org/pdf/2201.06386) 47 | * Visual analysis of discrimination in machine learning (TVCG 2021) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9222272) 48 | * FairRankVis: A visual analytics framework for exploring algorithmic fairness in graph mining models (TVCG 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552229) 49 | * DECE: Decision explorer with counterfactual explanations for machine learning models (TVCG 2021) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9229232) 50 | * Visual Auditor: Interactive visualization for detection and summarization of model biases (VIS 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9973204) 51 | * RMExplorer: A Visu-al Analytics Approach to Explore the Performance and the Fair-ness of Disease Risk Models on Population Subgroups (VIS 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9973226) 52 | * LINGO: Visually debiasing natural language instructions to support task diversity (CGF 2023) [pdf](https://arxiv.org/pdf/2304.06184) 53 | * D-BIAS: A causality-based human-in-the-loop system for tackling algorithmic bias (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9903601) 54 | * Parallel embeddings: A visualization technique for contrasting learned representations (IUI 2020) [pdf](https://dl.acm.org/doi/pdf/10.1145/3377325.3377514) 55 | * Explaining vulnerabilities to adversarial machine learning through visual analytics (TVCG 2020) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8812988) 56 | * Visual analytics of neuron vulnerability to adversarial attacks on convolutional neural networks (TiiS 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3587470) 57 | * ConceptExplainer: Interactive explanation for deep neural networks from a concept perspective (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9903285) 58 | * DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation (EuroVis 2022) [pdf](https://diglib.eg.org/bitstream/handle/10.2312/evs20221099/091-095.pdf?sequence=1&isAllowed=y) 59 | * ESCAPE: Countering systematic errors from machine’s blind spots via interactive visual analysis (CHI 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3544548.3581373) 60 | * VATLD: A visual analytics system to assess, understand and improve traffic light detection (TVCG 2021) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9233993 ) 61 | * Where can we help? A visual analytics approach to diagnosing and improving semantic segmentation of movable objects (TVCG 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552909) 62 | * A unified understanding of deep NLP models for text classification (TVCG 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9801603) 63 | * ShortcutLens: A visual analytics approach for exploring shortcuts in natural language understanding dataset (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10015807) 64 | * ScatterShot: Interactive in-context example curation for text transformation (IUI 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3581641.3584059) 65 | * SliceTeller: A data slice-driven approach for machine learning model validation (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9906903) 66 | * HardVis: Visual analytics to handle instance hardness using undersampling and oversampling techniques (CGF 2023) [pdf](https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14726) 67 | * ConceptExplorer: Visual analysis of concept drifts in multi-source time-series data (VAST 2020) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9308627 ) 68 | * Diagnosing concept drift with visual analytics (VAST 2020) [pdf]( https://arxiv.org/pdf/2007.14372.pdf ) 69 | * Angler: Helping machine translation practitioners prioritize model improvements (CHI 2023) [pdf](https://dl.acm.org/doi/pdf/10.1145/3544548.3580790) 70 | * Visual drift detection for event sequence data of business processes (TVCG 2022) [pdf]( https://arxiv.org/pdf/2011.09130.pdf ) 71 | * Improving the usability of hierarchical representations for interactively labeling large image data sets (HCI 2011) [pdf]( http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.3459&rep=rep1&type=pdf ) 72 | * Visual analytics for mobile eye tracking (TVCG 2017) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7539297 ) 73 | * VIAN: A visual annotation tool for film analysis (CGF 2019) [pdf]( https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13676 ) 74 | * VASSL: A visual analytics toolkit for social spambot labeling (TVCG 2020) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8805463 ) 75 | * IRVINE: A design study on analyzing correlation patterns of electrical engines (TVCG 2022) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552903 ) 76 | * Spatial labeling: Leveraging spatial layout for improving label quality in non-expert image annotation (CHI 2021) [pdf](https://dl.acm.org/doi/pdf/10.1145/3411764.3445165) 77 | * Mediatable: Interactive categorization of multimedia collections (CG&A 2010) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5473200 ) 78 | * Director's cut: Analysis and annotation of soccer matches (CG&A 2016) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7579433 ) 79 | * Visual concept programming: A visual analytics approach to injecting human intelligence at scale (TVCG 2023) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9904017) 80 | * Comparing person-and process-centric strategies for obtaining quality data on amazon mechanical turk (CHI 2015) [pdf](https://dl.acm.org/doi/pdf/10.1145/2702123.2702553) 81 | * Revolt: Collaborative crowdsourcing for labeling machine learning datasets (CHI 2017) [pdf](https://dl.acm.org/doi/pdf/10.1145/3025453.3026044) ♠ 82 | * Rehumanized crowdsourcing: A labeling framework addressing bias and ethics in machine learning (CHI 2019) [pdf](https://dl.acm.org/doi/pdf/10.1145/3290605.3300773) 83 | * Crowdsourcing multi-label audio annotation tasks with citizen scientists (CHI 2019) [pdf](https://dl.acm.org/doi/pdf/10.1145/3290605.3300522) 84 | * Project sidewalk: A web-based crowdsourcing tool for collecting sidewalk accessibility data at scale (CHI 2019) [pdf](https://dl.acm.org/doi/pdf/10.1145/3290605.3300292) 85 | * Eliciting confidence for improving crowdsourced audio annotations (HCI 2022) [pdf](https://dl.acm.org/doi/pdf/10.1145/3512935) 86 | * Extracting references between text and charts via crowdsourcing (CHI 2014) [pdf](https://dl.acm.org/doi/pdf/10.1145/2556288.2557241) 87 | * Jury learning: Integrating dissenting voices into machine learning models (CHI 2022) [pdf](https://dl.acm.org/doi/pdf/10.1145/3491102.3502004) 88 | * Inter-active learning of ad-hoc classifiers for video visual analytics (VAST 2012) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6400492 ) 89 | * FDive: Learning relevance models using pattern-based similarity measures (VAST 2019) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8986940 ) 90 | * Visual classifier training for text document retrieval (TVCG 2012) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6327290 ) 91 | * Active learning and visual analytics for stance classification with ALVA (TiiS 2017) [pdf](https://dl.acm.org/doi/pdf/10.1145/3132169) 92 | * Analytic: An active learning system for trajectory classification (CG&A 2017) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8047427 ) 93 | * Aila: Attentive interactive labeling assistant for document classification through attention-based deep neural networks (CHI 2019) [pdf](https://dl.acm.org/doi/pdf/10.1145/3290605.3300460) 94 | * Peax: Interactive visual pattern search in sequential data using unsupervised deep representation learning (CGF 2020) [pdf]( https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13971 ) 95 | * Interactive learning for identifying relevant tweets to support real-time situational awareness (TVCG 2020) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8807283 ) 96 | * VIANA: Visual interactive annotation of argumentation (VAST 2019) [pdf]( https://arxiv.org/pdf/1907.12413 ) 97 | * Diagnosing ensemble few-shot classifiers (TVCG 2022) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9795241) 98 | * Towards visual explainable active learning for zero-shot classification (TVCG 2022) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552842 ) 99 | * Comparing visual-interactive labeling with active learning: An experimental study (TVCG 2018) [pdf]( http://eprints.cs.univie.ac.at/5257/1/bernard2017labeling.pdf ) 100 | * Towards user-centered active learning algorithms (CGF 2018) [pdf]( https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13406 ) 101 | * A visual analytics framework for explaining and diagnosing transfer learning processes (TVCG 2021) [pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9219240) 102 | * Designing interactive transfer learning tools for ML non-experts (CHI 2021) [pdf](https://dl.acm.org/doi/pdf/10.1145/3411764.3445096) 103 | * Polyphony: An interactive transfer learning framework for single-cell data analysis (TVCG 2023) [pdf](https://ieeexplore.ieee.org/iel7/2945/4359476/09903604.pdf) 104 | * Interactive graph construction for graph-based semi-supervised learning (TVCG 2021) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9444198 ) 105 | * Towards better caption supervision for object detection (TVCG 2022) [pdf]( https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9664269 ) 106 | * Sparks of artificial general intelligence: Early experiments with gpt-4 (arXiv 2023) [pdf](https://arxiv.org/pdf/2303.12712) 107 | * GPT-4 technical report (arXiv 2023) [pdf](https://arxiv.org/pdf/2303.08774) 108 | * Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning (NIPS 2022) [pdf](https://proceedings.neurips.cc/paper_files/paper/2022/file/0cde695b83bd186c1fd456302888454c-Paper-Conference.pdf) 109 | * Rethinking the role of demonstrations: What makes in-context learning work? (EMNLP 2022) [pdf](https://aclanthology.org/2022.emnlp-main.759.pdf) 110 | * What makes good in-context examples for GPT-3? (DeeLIO 2022) [pdf](https://aclanthology.org/2022.deelio-1.10.pdf) ♠ 111 | * Ground-truth labels matter: A deeper look into input-label demonstrations (EMNLP 2022) [pdf](https://aclanthology.org/2022.emnlp-main.155.pdf) 112 | * Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity (ACL 2022) [pdf](https://aclanthology.org/2022.acl-long.556.pdf) -------------------------------------------------------------------------------- /paper.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thu-vis/Visual-Analytics-Data-Quality/ee72812d743329df5f270f8d757629c0c9c68ffa/paper.pdf --------------------------------------------------------------------------------