Papers with dataset generation
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. |
Prompt2Model: Generating Deployable Models from Natural Language Instructions (2023.emnlp-demo)
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| Challenge: | Large language models (LLMs) are a step backward from traditional special-purpose NLP models . they require extensive computational resources for deployment and can be gated behind APIs . |
| Approach: | They propose a general-purpose method that takes a natural language task description and uses it to train a special-purpose model. |
| Outcome: | The proposed method outperforms a strong LLM by 20% while being 700 times smaller. |
Improving Paraphrase Detection with the Adversarial Paraphrasing Task (2021.acl-long)
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| Challenge: | a new adversarial method of paraphrase identification is being used to identify paraphrases based on word overlap and syntax . authors propose a dataset that generates semantically equivalent but lexically and syntactically disparate paraphrase pairs . |
| Approach: | They propose an adversarial method for paraphrase identification that uses word overlap and syntax to identify paraphrases. |
| Outcome: | The proposed method improves paraphrase detection accuracy and speed of generation of datasets. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
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. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence Checking (2026.findings-acl)
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| Challenge: | a new benchmarking tool for code equivalence checks the performance of LLMs. |
| Approach: | They propose a code-equivalence with transformations benchmark built from a repository of programs that may solve the same or different tasks. |
| Outcome: | The proposed approach boosts performance on the transformed pairs of programs. |