{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/prompt-learning/papers/7","list_of":"/task/prompt-learning","task":"Prompt Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":7,"pages_in_order":7,"rows_per_page":100,"rows":[601,678],"of":678,"counts":{"archive_papers_tagged":678,"with_a_code_link":320,"where_syntology_ran_a_sample":116,"not_listed_spam_title":0,"listed":678,"listed_where_code_ran":116,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":100,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":100,"listed_every_run_a_failure_of_syntologys_instrument":16,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/prompt-learning","prev":"/task/prompt-learning/papers/6","next":null,"papers":[{"url":null,"slug":"prompt-tuning-large-language-models-on","title":"Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations","date":"2023-06-02","arxiv_id":"2306.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"cooperative-hardware-prompt-learning-for","title":"Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging","date":"2023-06-01","arxiv_id":"2306.01176","repositories_listed":0,"syntology":null},{"url":"/paper/trompt-towards-a-better-deep-neural-network","slug":"trompt-towards-a-better-deep-neural-network","title":"Trompt: Towards a Better Deep Neural Network for Tabular Data","date":"2023-05-29","arxiv_id":"2305.18446","repositories_listed":0,"syntology":{"n":6,"n_ran":6,"n_constructed":4,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/trompt-towards-a-better-deep-neural-network#ran","syntology_url":"https://syntology.ai/paper/2305.18446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18446"}},"official":null}},{"url":null,"slug":"on-the-robustness-of-segment-anything","title":"On the Robustness of Segment Anything","date":"2023-05-25","arxiv_id":"2305.16220","repositories_listed":0,"syntology":null},{"url":null,"slug":"flocks-of-stochastic-parrots-differentially","title":"Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models","date":"2023-05-24","arxiv_id":"2305.15594","repositories_listed":0,"syntology":null},{"url":"/paper/prompt-learning-for-action-recognition","slug":"prompt-learning-for-action-recognition","title":"SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition","date":"2023-05-21","arxiv_id":"2305.12437","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-vlgm-bi-level-class-severity-aware-vision","title":"Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation","date":"2023-05-20","arxiv_id":"2305.12231","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approaches-to-lexical","title":"Deep Learning Approaches to Lexical Simplification: A Survey","date":"2023-05-19","arxiv_id":"2305.12000","repositories_listed":0,"syntology":null},{"url":null,"slug":"compress-then-prompt-improving-accuracy","title":"Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt","date":"2023-05-17","arxiv_id":"2305.11186","repositories_listed":0,"syntology":null},{"url":null,"slug":"msprompt-multi-step-prompt-learning-for","title":"MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection","date":"2023-05-16","arxiv_id":"2305.09335","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-tuning-decision-transformer-with","title":"Prompt-Tuning Decision Transformer with Preference Ranking","date":"2023-05-16","arxiv_id":"2305.09648","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-multi-granularity-academic","title":"Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge","date":"2023-05-05","arxiv_id":"2305.03287","repositories_listed":0,"syntology":null},{"url":null,"slug":"rplkg-robust-prompt-learning-with-knowledge","title":"RPLKG: Robust Prompt Learning with Knowledge Graph","date":"2023-04-21","arxiv_id":"2304.10805","repositories_listed":0,"syntology":null},{"url":null,"slug":"ceil-a-general-classification-enhanced","title":"CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering","date":"2023-04-20","arxiv_id":"2304.11061","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-vision-prompt-fusion-network-can","title":"Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?","date":"2023-04-20","arxiv_id":"2304.10224","repositories_listed":0,"syntology":null},{"url":null,"slug":"supporting-qualitative-analysis-with-large","title":"Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding","date":"2023-04-17","arxiv_id":"2304.10548","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-prompts-on-vision-language","title":"Towards Robust Prompts on Vision-Language Models","date":"2023-04-17","arxiv_id":"2304.08479","repositories_listed":0,"syntology":null},{"url":null,"slug":"pbnr-prompt-based-news-recommender-system","title":"PBNR: Prompt-based News Recommender System","date":"2023-04-16","arxiv_id":"2304.07862","repositories_listed":0,"syntology":null},{"url":"/paper/mvp-seg-multi-view-prompt-learning-for-open","slug":"mvp-seg-multi-view-prompt-learning-for-open","title":"MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation","date":"2023-04-14","arxiv_id":"2304.06957","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-prompting-for-non-overlapping-cross","title":"Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation","date":"2023-04-09","arxiv_id":"2304.04218","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-aware-multimodal-prompt-learning","title":"Similarity-Aware Multimodal Prompt Learning for Fake News Detection","date":"2023-04-09","arxiv_id":"2304.04187","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-brain-tumor-segmentation-with","title":"Unsupervised Brain Tumor Segmentation with Image-based Prompts","date":"2023-04-04","arxiv_id":"2304.01472","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-prompt-learning-for-dense","title":"Probabilistic Prompt Learning for Dense Prediction","date":"2023-04-03","arxiv_id":"2304.00779","repositories_listed":0,"syntology":null},{"url":"/paper/freeseg-unified-universal-and-open-vocabulary","slug":"freeseg-unified-universal-and-open-vocabulary","title":"FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation","date":"2023-03-30","arxiv_id":"2303.17225","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-prompt-learning-for-unsupervised","title":"Iterative Prompt Learning for Unsupervised Backlit Image Enhancement","date":"2023-03-30","arxiv_id":"2303.17569","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-multi-modal-mutual-leaning-for","title":"Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models","date":"2023-03-30","arxiv_id":"2303.17169","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-expressive-prompting-with-residuals","title":"Learning Expressive Prompting With Residuals for Vision Transformers","date":"2023-03-27","arxiv_id":"2303.15591","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-for-all-things-zero-shot-sketch-based","title":"CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not","date":"2023-03-23","arxiv_id":"2303.13440","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-name-is-mantra-unifying-point-cloud","title":"Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets","date":"2023-03-19","arxiv_id":"2303.10585","repositories_listed":0,"syntology":null},{"url":null,"slug":"lion-implicit-vision-prompt-tuning","title":"LION: Implicit Vision Prompt Tuning","date":"2023-03-17","arxiv_id":"2303.09992","repositories_listed":0,"syntology":null},{"url":null,"slug":"translating-radiology-reports-into-plain","title":"Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential","date":"2023-03-16","arxiv_id":"2303.09038","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-graph-prompting-methods","title":"A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges","date":"2023-03-13","arxiv_id":"2303.07275","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-regulated-meta-prompt-learning-for","title":"Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models","date":"2023-03-12","arxiv_id":"2303.06571","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-visual-prompt-learning-to-zero-shot","title":"From Visual Prompt Learning to Zero-Shot Transfer: Mapping Is All You Need","date":"2023-03-09","arxiv_id":"2303.05266","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-tuning-regularized-prompt-tuning-in-open","title":"M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios","date":"2023-03-09","arxiv_id":"2303.05122","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-visual-prompt-learning-as-masked","title":"Rethinking Visual Prompt Learning as Masked Visual Token Modeling","date":"2023-03-09","arxiv_id":"2303.04998","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-sampling-for-masked-language","title":"Weighted Sampling for Masked Language Modeling","date":"2023-02-28","arxiv_id":"2302.14225","repositories_listed":0,"syntology":null},{"url":null,"slug":"stylip-multi-scale-style-conditioned-prompt","title":"StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization","date":"2023-02-18","arxiv_id":"2302.09251","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-protein-structure-pre-training","title":"Multi-level Protein Structure Pre-training via Prompt Learning","date":"2023-02-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-retrospect-to-multi-prompt-learning-across","title":"A Retrospect to Multi-prompt Learning across Vision and Language","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"decouple-before-interact-multi-modal-prompt","title":"Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-prompt-learning-for-multi-task","title":"Hierarchical Prompt Learning for Multi-Task Learning","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"decorate-the-newcomers-visual-domain-prompt","title":"Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation","date":"2022-12-08","arxiv_id":"2212.04145","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptonomyvit-multi-task-prompt-learning","title":"PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data","date":"2022-12-08","arxiv_id":"2212.04821","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-using-few-shot-prompt-learning-for","title":"Towards using Few-Shot Prompt Learning for Automating Model Completion","date":"2022-12-07","arxiv_id":"2212.03404","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-image-captioning-via-prompting","title":"Controllable Image Captioning via Prompting","date":"2022-12-04","arxiv_id":"2212.01803","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-device-collaborative-adaptation-to","title":"Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world","date":"2022-12-02","arxiv_id":"2212.00972","repositories_listed":0,"syntology":null},{"url":null,"slug":"tapping-the-potential-of-coherence-and","title":"Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring","date":"2022-11-24","arxiv_id":"2211.13373","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-domain-adaptation-in-task","title":"Prompt Learning for Domain Adaptation in Task-Oriented Dialogue","date":"2022-11-10","arxiv_id":"2211.05596","repositories_listed":0,"syntology":null},{"url":null,"slug":"consprompt-easily-exploiting-contrastive","title":"ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning","date":"2022-11-08","arxiv_id":"2211.04118","repositories_listed":0,"syntology":null},{"url":null,"slug":"stprompt-semantic-guided-and-task-driven","title":"STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification","date":"2022-10-29","arxiv_id":"2210.16489","repositories_listed":0,"syntology":null},{"url":null,"slug":"clip-tuning-towards-derivative-free-prompt","title":"Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards","date":"2022-10-21","arxiv_id":"2210.12050","repositories_listed":0,"syntology":null},{"url":null,"slug":"cpl-counterfactual-prompt-learning-for-vision","title":"CPL: Counterfactual Prompt Learning for Vision and Language Models","date":"2022-10-19","arxiv_id":"2210.10362","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-through-prototype-a-prototype-based","title":"Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models","date":"2022-10-19","arxiv_id":"2210.10841","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-preference-learning-for-storytelling","title":"Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning","date":"2022-10-14","arxiv_id":"2210.07792","repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-as-machine","title":"Aspect-based Sentiment Analysis as Machine Reading Comprehension","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ji-yu-zhu-ti-ti-shi-xue-xi-de-ling-yang-ben","title":"基于主题提示学习的零样本立场检测方法(A Topic-based Prompt Learning Method for Zero-Shot Stance Detection)","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rong-he-ti-shi-xue-xi-de-gu-shi-sheng-cheng","title":"融合提示学习的故事生成方法(A Story Generation Method Incorporating Prompt Learning)","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-driven-efficient-open-set-semi","title":"Prompt-driven efficient Open-set Semi-supervised Learning","date":"2022-09-28","arxiv_id":"2209.14205","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-generative-framework-based-on","title":"A Unified Generative Framework based on Prompt Learning for Various Information Extraction Tasks","date":"2022-09-23","arxiv_id":"2209.11570","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptattack-prompt-based-attack-for-language","title":"PromptAttack: Prompt-based Attack for Language Models via Gradient Search","date":"2022-09-05","arxiv_id":"2209.01882","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-matched-semantic-segmentation","title":"Prompt-Matched Semantic Segmentation","date":"2022-08-22","arxiv_id":"2208.10159","repositories_listed":0,"syntology":null},{"url":null,"slug":"promptgen-automatically-generate-prompts","title":"PromptGen: Automatically Generate Prompts using Generative Models","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-wise-prompt-tuning-for-pretrained","title":"Instance-wise Prompt Tuning for Pretrained Language Models","date":"2022-06-04","arxiv_id":"2206.01958","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-learning-for-unpaired-image","title":"Prompt-based Learning for Unpaired Image Captioning","date":"2022-05-26","arxiv_id":"2205.13125","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-4ever-lt-edi-acl2022-detecting-signs-of","title":"BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"impossible-triangle-what-s-next-for-pre","title":"Impossible Triangle: What's Next for Pre-trained Language Models?","date":"2022-04-13","arxiv_id":"2204.06130","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-short-text-classification","title":"Prompt-Learning for Short Text Classification","date":"2022-02-23","arxiv_id":"2202.11345","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaprompt-adaptive-model-training-for-prompt","title":"AdaPrompt: Adaptive Model Training for Prompt-based NLP","date":"2022-02-10","arxiv_id":"2202.04824","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-dynamic-resource-allocation","title":"PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications","date":"2022-01-19","arxiv_id":"2201.07916","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-prompt-customize-a-unique","title":"Context-Aware Prompt: Customize A Unique Prompt For Each Input","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-few-shot-dialogue-state","title":"A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking","date":"2022-01-15","arxiv_id":"2201.05780","repositories_listed":0,"syntology":null},{"url":null,"slug":"nsp-bert-a-prompt-based-zero-shot-learner-1","title":"NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nsp-ner-a-prompt-based-learner-for-few-shot","title":"NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-fine-grained-entity-1","title":"Prompt-Learning for Fine-Grained Entity Typing","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unified-prompt-tuning-for-few-shot","title":"Towards Unified Prompt Tuning for Few-shot Learning","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"response-generation-with-context-aware-prompt","title":"Response Generation with Context-Aware Prompt Learning","date":"2021-11-04","arxiv_id":"2111.02643","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-fine-grained-entity","title":"Prompt-Learning for Fine-Grained Entity Typing","date":"2021-08-24","arxiv_id":"2108.10604","repositories_listed":0,"syntology":null}],"record_sha256":"4b178ef4147ca4fcf8552a1e0802e79b8081b665b4d1bbc93934cc61722d6543","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}