mlsanityData to 7 Oct 2026

Lingua Manga: A Generic Large Language Model Centric System for Data Curation

arXiv 2306.11702 ↗First posted 20 Jun 2023cs.DB cs.CLcited by 8

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Abstract (arXiv)

Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks. However, the diversity of such tasks makes it challenging to develop a general-purpose data curation system. To address this issue, we present Lingua Manga, a user-friendly and versatile system that utilizes pre-trained large language models. Lingua Manga offers automatic optimization for achieving high performance and label efficiency while facilitating flexible and rapid development. Through three example applications with distinct objectives and users of varying levels of technical proficiency, we demonstrate that Lingua Manga can effectively assist both skilled programmers and low-code or even no-code users in addressing data curation challenges.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

12 of 20 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  6. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  7. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  8. 2022Can Foundation Models Wrangle Your Data?cited by 173
  9. 2020Deep Entity Matching with Pre-Trained Language Modelscited by 143
  10. 2020Few-Shot Text Generation with Pattern-Exploiting Trainingcited by 87
  11. 2017HoloClean: Holistic Data Repairs with Probabilistic Inferencecited by 79
  12. 2022A Survey on Pretrained Language Models for Neural Code Intelligencecited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 0; 2026: 1.

8 papers.

  1. 2024KeyVideoLLM: Towards Large-scale Video Keyframe Selectioncited by 52
  2. 2024BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batchingcited by 31
  3. 2024Synth-Empathy: Towards High-Quality Synthetic Empathy Datacited by 10
  4. 2024PAS: Data-Efficient Plug-and-Play Prompt Augmentation Systemcited by 7
  5. 2024SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Modelscited by 6
  6. 2026Towards Next-Generation LLM Training: From the Data-Centric Perspectivecited by 3
  7. 2024Data Proportion Detection for Optimized Data Management for Large Language Modelscited by 1
  8. 2024Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Datacited by 0