Incubating Text Classifiers Following User Instruction with Nothing but LLM
Abstract: In this paper, we aim to generate text classification data given arbitrary class definitions (i.e., user instruction), so one can train a text classifier without any human annotation or raw corpus.
Recent advances in large language models (LLMs) lead to pioneer attempts to individually generate texts for each class via prompting.
In this paper, we propose Incubator, the first framework that can handle complicated and even mutually dependent classes (e.g., "\emph{TED Talk given by Educator}" and "\emph{Other}").
Specifically, our Incubator is a fine-tuned LLM that takes the instruction of all class definitions as input, and in each inference, it can jointly generate one sample for every class.
First, we tune Incubator on the instruction-to-data mappings that we obtained from classification datasets and descriptions on Hugging Face together with in-context augmentation by GPT-4.
To emphasize the uniformity and diversity in generations, we refine Incubator by fine-tuning with the cluster centers of semantic textual embeddings of the generated samples.
We compare Incubator on various classification tasks with strong baselines such as direct LLM-based inference and training data generation by prompt engineering.
Experiments show Incubator is able to (1) outperform previous methods on traditional benchmarks, (2) take label interdependency and user preference into consideration, and (3) enable logical text mining by incubating multiple classifiers.
Paper Type: Long
Research Area: Efficient/Low-Resource Methods for NLP
Research Area Keywords: Model Incubation, Text Classification, Data Generation, Data Diversity
Contribution Types: Approaches to low-resource settings
Languages Studied: English
Submission Number: 3676
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