Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs (2023.emnlp-demo)
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| Challenge: | Recent research addresses the bottleneck of producing labeled training data for NLP tasks. |
| Approach: | They propose a method that generates labeled data that can be used to train a downstream NLP model. |
| Outcome: | The proposed model enables an LLM to generate labeled data that can be used to train a downstream NLP model. |
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| Challenge: | Existing methods to generate unlabeled text are difficult to find. |
| Approach: | They propose a general framework called "generate, annotate, and learn" to take advantage of synthetic text within knowledge distillation, self-training, and few-shot learning applications. |
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Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, making them promising tools in both high- and low-resource languages. |
| Approach: | They use a multilingual LLM to generate synthetic datasets covering 11 languages and 4 classification tasks and use them to train smaller models. |
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Making Large Language Models Better Data Creators (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have advanced the field of NLP significantly, but deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. |
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DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows (2024.acl-long)
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| Challenge: | Large language models (LLMs) have become a dominant tool for NLP researchers in a wide range of tasks. |
| Approach: | They propose an open source Python library that allows researchers to write simple code to implement powerful LLM workflows. |
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
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Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)
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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
| Challenge: | Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas. |
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LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)
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| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
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API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs (2024.acl-long)
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Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Vernon Austel, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis Lastras
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ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval (2023.findings-acl)
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| Challenge: | Recent studies show that large pretrained language models can generate training data with no task-specific or cross-task data. |
| Approach: | They propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. |
| Outcome: | The proposed framework achieves 4.3% gain over baselines and saves 70% of time compared with baselines using large language models. |
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)
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| Challenge: | a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size. |
| Approach: | They propose a method which leverages the generative power of large language models to train a smaller model. |
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