---
title: "Increasing Diversity While Maintaining Accuracy: Text Data Generation — paper by Chung et al. (2023)"
description: "They compare two decoding-level ways to diversify output across eight classification tasks. 47 references, cited by 74 papers here."
authors: ["John Chung", "Ece Kamar", "Saleema Amershi"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2023.acl-long.34/"
doi: "10.18653/v1/2023.acl-long.34"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/"
---
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# Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

**Authors:** John Chung, [Ece Kamar](https://md.research.elamur.ai/free/en/authors/ece-kamar/) and Saleema Amershi · **Year:** 2023 · **Venue:** [Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2023](https://md.research.elamur.ai/free/en/venues/acl/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.acl-long.34/) · [PDF](https://aclanthology.org/2023.acl-long.34.pdf) · [DOI 10.18653/v1/2023.acl-long.34](https://doi.org/10.18653/v1/2023.acl-long.34) · [OpenAlex](https://openalex.org/W4385570982)

**Citations:** 67 citations (OpenAlex, 2026-10-14) · [74](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Custom text classifiers often lack suitable training data, since existing corpora may not fit the target domain or label set and new annotation is costly. The authors explore using an LLM (GPT-3, text-davinci-002) to synthesize labeled data that is both varied and correct, and then fine-tuning a cheaper standalone classifier on it. They compare two decoding-level ways to diversify output across eight classification tasks. The first, logit suppression, penalizes tokens that are already frequent in the growing dataset. The second raises the sampling temperature. Both make the corpus more diverse, but both also produce more items with mismatched labels or off-domain content. To address this, the authors use oracle experiments to simulate two human interventions: label replacement (LR), which corrects wrong labels, and out-of-scope filtering (OOSF), which discards irrelevant items. LR substantially improved downstream classifiers, while OOSF gave no consistent gain. The results matter for teams that cannot or prefer not to call external LLM APIs at inference time. They also show where limited human effort pays off in synthetic-data pipelines.

## Key points
- The evaluation covers eight classification datasets, including SST-2, Clickbait (CB), CARER, and PubMed 200k RCT, with GPT-3 text-davinci-002 as the generator and a single prompt template ("Prompt A").
- Combining logit suppression with high temperature and then fixing all incorrect labels via LR raised the absolute accuracy of trained models by 14.4%.
- Without LR, models trained on generated data averaged below GPT-3 few-shot classification. After about 180 label-replaced instances, they began to surpass it.
- OOSF slightly improved label accuracy and similarity to the original data but reduced diversity. Its effect on model accuracy varied by task, possibly because filtering removed useful boundary examples.
- Stated limitations include label imbalance from applying proxy models only after generation, testing only one LLM, and possible inherited LLM bias.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 240 works · 123,537 citations
- [University of Michigan](https://md.research.elamur.ai/free/en/universities/university-of-michigan/) — University, Ann Arbor, United States · 36 works · 12,181 citations

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## Cite

Cite as: John Chung, Ece Kamar and Saleema Amershi. 2023. Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chung2023increasing,
  title = {{Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions}},
  author = {John Chung, Ece Kamar and Saleema Amershi},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.34},
  url = {https://doi.org/10.18653/v1/2023.acl-long.34}
}
```

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47. Equation for Temperature Sampling Mathematically, with the temperature T and original probability of token, p i , the temperature sampled probability of token i, f T (p) i , would be denoted as below: 1/T f T (p) i = p i 1/T Σ j p j [find](https://scholar.google.com/scholar?q=Equation+for+Temperature+Sampling+Mathematically%2C+with+the+temperature+T+and+original+probability+of+token%2C+p+i+%2C+the+temperature+sampled+probability+of+token+i%2C+f+T+%28p%29+i+%2C+would+be+denoted+as+below%3A)

*Updated 24 October 2026*