---
title: "Synthetic Data Generation for Training Diversified Commonsense"
description: "The authors present CommonSyn, which they describe as the first synthetic dataset built for diversified GCR. 60 references."
authors: ["Tianhui Zhang", "Bei Peng", "Danushka Bollegala"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1520/"
doi: "10.18653/v1/2026.acl-long.1520"
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/zhang-2026-synthetic-data-generation-training-diversified/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2026-synthetic-data-generation-training-diversified/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/51/) / Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

# Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

**Authors:** [Tianhui Zhang](https://md.research.elamur.ai/free/en/authors/tianhui-zhang/), Bei Peng and [Danushka Bollegala](https://md.research.elamur.ai/free/en/authors/danushka-bollegala/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **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/2026.acl-long.1520/) · [PDF](https://aclanthology.org/2026.acl-long.1520.pdf) · [DOI 10.18653/v1/2026.acl-long.1520](https://doi.org/10.18653/v1/2026.acl-long.1520) · [OpenAlex](https://openalex.org/W7166799156)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Generative Commonsense Reasoning (GCR) asks a model to write plausible sentences from a small set of concepts. Ideally it should produce several sentences that are each sound and that together describe varied scenarios. Progress has been held back by scarce training data. The standard CommonGen training set was built by few annotators, and most of its concept sets have only one or two reference sentences. The authors present CommonSyn, which they describe as the first synthetic dataset built for diversified GCR. It is created in two stages:
1. **Concept expansion.** Two anchor concepts are taken from an existing CommonGen set, and an LLM adds bridge concepts to form a new scenario.
2. **Generation and selection.** Candidate sentences are produced with several prompting styles. A subset is then chosen by a procedure that weighs quality, diversity within each concept set, and diversity across the whole dataset.

Across many LLM families and sizes, fine-tuning on CommonSyn improves quality and diversity together. It beats both untuned models and models tuned on human-written CommonGen data, and the gains carry over to related GCR tasks. This suggests synthetic data can ease the usual quality–diversity trade-off.

## Key points
- **Dataset size.** CommonSyn has 20,742 concept sets and 83,184 sentences, about 4 per set with an average length of roughly 14 words. CommonGen train has 32,651 sets and 67,389 sentences, about 2 per set.
- **Concept expansion.** New concept sets are built from 2 seed concepts plus 1–3 LLM-proposed bridge concepts. This relies on the observation that co-occurring CommonGen concepts tend to lie within 2 hops in ConceptNet.
- **Sentence generation.** Three strategies are used: dynamic few-shot, multi-sentence few-shot, and CoT-guided prompting. Each produces N=4 candidates per set, capped at 22 words.
- **Models.** Qwen2.5-72B-Instruct generates the data and Gemini-2.5-Flash scores quality. GPT-4o judges model outputs against human references using win–tie rates.
- **Evaluation and limitations.** Experiments cover 11 LLMs, including the Qwen-2.5 and Gemma-3 series, and test transfer to α-NLG (abductive reasoning), ComVE and ROCStories. The dataset is English-only. Pairwise similarity computation may limit scaling to very large candidate pools.

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

## Institutions

- [University of Sheffield](https://md.research.elamur.ai/free/en/universities/university-of-sheffield/) — University, Sheffield, United Kingdom · 117 works · 22,645 citations
- [University of Liverpool](https://md.research.elamur.ai/free/en/universities/university-of-liverpool/) — University, Liverpool, United Kingdom · 16 works · 745 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

## Cite

Cite as: Tianhui Zhang, Bei Peng and Danushka Bollegala. 2026. Synthetic Data Generation for Training Diversified Commonsense Reasoning Models. Proceedings of the 64th 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{zhang2026synthetic,
  title = {{Synthetic Data Generation for Training Diversified Commonsense Reasoning Models}},
  author = {Tianhui Zhang, Bei Peng and Danushka Bollegala},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1520},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1520}
}
```

- [← previous SynthAgent: Adapting Web Agents with Synthetic Supervision](https://md.research.elamur.ai/free/en/papers/wang-2026-synthagent-adapting-web-agents-synthetic/)
- [next → Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/schaffelder-2026-synthetic-eggs-many-baskets-impact/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/51/)

## References

60 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L Leavitt, and Mansheej Paul. 2024. Perplexed by perplexity: Perplexity-based data pruning with small reference models. arXiv preprint arXiv:2405.20541. — [arXiv:2405.20541](https://arxiv.org/abs/2405.20541) [find](https://scholar.google.com/scholar?q=Zachary+Ankner%2C+Cody+Blakeney%2C+Kartik+Sreenivasan%2C+Max+Marion%2C+Matthew+L+Leavitt%2C+and+Mansheej+Paul%2E+2024%2E+Perplexed+by+perplexity%3A+Perplexity%2Dbased+data+pruning+with+small+reference+models%2E+arXiv+pre)
2. André Bauer, Simon Trapp, Michael Stenger, Robert Leppich, Samuel Kounev, Mark Leznik, Kyle Chard, and Ian Foster. 2024. Comprehensive exploration of synthetic data generation: A survey. arXiv preprint arXiv:2401.02524. — [arXiv:2401.02524](https://arxiv.org/abs/2401.02524) [find](https://scholar.google.com/scholar?q=Andr%C3%A9+Bauer%2C+Simon+Trapp%2C+Michael+Stenger%2C+Robert+Leppich%2C+Samuel+Kounev%2C+Mark+Leznik%2C+Kyle+Chard%2C+and+Ian+Foster%2E+2024%2E+Comprehensive+exploration+of+synthetic+data+generation%3A+A+survey%2E+arXiv+preprin)
3. Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen tau Yih, and Yejin Choi. 2020. [Abductive commonsense reasoning](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/). Preprint, arXiv:1908.05739.
4. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. [Piqa: Reasoning about physical commonsense in natural language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/). In Thirty-Fourth AAAI Conference on Artificial Intelligence.
5. Hao Chen, Abdul Waheed, Xiang Li, Yidong Wang, Jindong Wang, Bhiksha Raj, and Marah I Abdin. 2024a. [On the diversity of synthetic data and its impact on training large language models](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/). arXiv preprint arXiv:2410.15226.
6. Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, and 1 others. 2023. [Alpagasus: Training a better alpaca with fewer data](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/). arXiv preprint arXiv:2307.08701.
7. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. 2024b. Self-play fine-tuning con-vertsweak language models to strong language models. In Proceedings of the 41st International Conference on Machine Learning, pages 6621–6642. [find](https://scholar.google.com/scholar?q=Zixiang+Chen%2C+Yihe+Deng%2C+Huizhuo+Yuan%2C+Kaixuan+Ji%2C+and+Quanquan+Gu%2E+2024b%2E+Self%2Dplay+fine%2Dtuning+con%2Dvertsweak+language+models+to+strong+language+models%2E+In+Proceedings+of+the+41st+International+Confe)
8. Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul, Christian Von Der Weth, and Brian Y. Lim. 2021. Directed diversity: Leveraging language embedding distances for collective creativity in crowd ideation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–35. [find](https://scholar.google.com/scholar?q=Samuel+Rhys+Cox%2C+Yunlong+Wang%2C+Ashraf+Abdul%2C+Christian+Von+Der+Weth%2C+and+Brian+Y%2E+Lim%2E+2021%2E+Directed+diversity%3A+Leveraging+language+embedding+distances+for+collective+creativity+in+crowd+ideation%2E+In)
9. Wanqing Cui, Keping Bi, Jiafeng Guo, and Xueqi Cheng. 2024. More: Multi-modal retrieval augmented generative commonsense reasoning. In Findings of the Association for Computational Linguistics ACL 2024, pages 1178–1192. [find](https://scholar.google.com/scholar?q=Wanqing+Cui%2C+Keping+Bi%2C+Jiafeng+Guo%2C+and+Xueqi+Cheng%2E+2024%2E+More%3A+Multi%2Dmodal+retrieval+augmented+generative+commonsense+reasoning%2E+In+Findings+of+the+Association+for+Computational+Linguistics+ACL+202)
10. Michael Han Daniel Han and Unsloth team. 2023. Unsloth. [find](https://scholar.google.com/scholar?q=Michael+Han+Daniel+Han+and+Unsloth+team%2E+2023%2E+Unsloth%2E)
11. Zhiwei Deng, Tao Li, and Yang Li. 2024. Influential language data selection via gradient trajectory pursuit. arXiv preprint arXiv:2410.16710. — [arXiv:2410.16710](https://arxiv.org/abs/2410.16710) [find](https://scholar.google.com/scholar?q=Zhiwei+Deng%2C+Tao+Li%2C+and+Yang+Li%2E+2024%2E+Influential+language+data+selection+via+gradient+trajectory+pursuit%2E+arXiv+preprint+arXiv%3A2410%2E16710%2E+%E2%80%94+%5BarXiv%3A2410%2E16710%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2410%2E16710%29)
12. Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, and Julia Kempe. 2025. Beyond model collapse: Scaling up with synthesized data requires verification. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yunzhen+Feng%2C+Elvis+Dohmatob%2C+Pu+Yang%2C+Francois+Charton%2C+and+Julia+Kempe%2E+2025%2E+Beyond+model+collapse%3A+Scaling+up+with+synthesized+data+requires+verification%2E+In+The+Thirteenth+International+Conferenc)
13. Dan Friedman and Adji Bousso Dieng. 2023. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Dan+Friedman+and+Adji+Bousso+Dieng%2E+2023%2E+The+vendi+score%3A+A+diversity+evaluation+metric+for+machine+learning%2E+Transactions+on+Machine+Learning+Research%2E)
14. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. [Simcse: Simple contrastive learning of sentence embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6894–6910.
15. Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. 2024. [Scaling synthetic data creation with 1,000,000,000 personas](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/). arXiv preprint arXiv:2406.20094.
16. Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, and 1 others. 2024. Deepseek-coder: When the large language model meets programming–the rise of code intelligence. arXiv preprint arXiv:2401.14196. [find](https://scholar.google.com/scholar?q=Daya+Guo%2C+Qihao+Zhu%2C+Dejian+Yang%2C+Zhenda+Xie%2C+Kai+Dong%2C+Wentao+Zhang%2C+Guanting+Chen%2C+Xiao+Bi%2C+Yu+Wu%2C+YK+Li%2C+and+1+others%2E+2024%2E+Deepseek%2Dcoder%3A+When+the+large+language+model+meets+programming%E2%80%93the+rise)
17. Tim Hesterberg. 2011. Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3(6):497– 526. [find](https://scholar.google.com/scholar?q=Tim+Hesterberg%2E+2011%2E+Bootstrap%2E+Wiley+Interdisciplinary+Reviews%3A+Computational+Statistics%2C+3%286%29%3A497%E2%80%93+526%2E)
18. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. [The curious case of neural text degeneration](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/). arXiv preprint arXiv:1904.09751.
19. Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, and Weizhu Chen. 2025. Key-point-driven data synthesis with its enhancement on mathematical reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24176–24184. [find](https://scholar.google.com/scholar?q=Yiming+Huang%2C+Xiao+Liu%2C+Yeyun+Gong%2C+Zhibin+Gou%2C+Yelong+Shen%2C+Nan+Duan%2C+and+Weizhu+Chen%2E+2025%2E+Key%2Dpoint%2Ddriven+data+synthesis+with+its+enhancement+on+mathematical+reasoning%2E+In+Proceedings+of+the+AAAI)
20. EunJeong Hwang, Veronika Thost, Vered Shwartz, and Tengfei Ma. 2023. Knowledge graph compression enhances diverse commonsense generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 558–572, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=EunJeong+Hwang%2C+Veronika+Thost%2C+Vered+Shwartz%2C+and+Tengfei+Ma%2E+2023%2E+Knowledge+graph+compression+enhances+diverse+commonsense+generation%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+)
21. Jaehun Jung, Seungju Han, Ximing Lu, Skyler Hallinan, David Acuna, Shrimai Prabhumoye, Mostafa Patwary, Mohammad Shoeybi, Bryan Catanzaro, and Yejin Choi. 2025. Prismatic synthesis: Gradient-based data diversification boosts generalization in llm reasoning. arXiv preprint arXiv:2505.20161. — [arXiv:2505.20161](https://arxiv.org/abs/2505.20161) [find](https://scholar.google.com/scholar?q=Jaehun+Jung%2C+Seungju+Han%2C+Ximing+Lu%2C+Skyler+Hallinan%2C+David+Acuna%2C+Shrimai+Prabhumoye%2C+Mostafa+Patwary%2C+Mohammad+Shoeybi%2C+Bryan+Catanzaro%2C+and+Yejin+Choi%2E+2025%2E+Prismatic+synthesis%3A+Gradient%2Dbased+dat)
22. Masahiro Kaneko and D Bollegala. 2021. Unmasking the mask - evaluating social biases in masked language models. National Conference on Artificial Intelligence, pages 11954–11962. [find](https://scholar.google.com/scholar?q=Masahiro+Kaneko+and+D+Bollegala%2E+2021%2E+Unmasking+the+mask+%2D+evaluating+social+biases+in+masked+language+models%2E+National+Conference+on+Artificial+Intelligence%2C+pages+11954%E2%80%9311962%2E)
23. Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, and Naoaki Okazaki. 2022. Gender bias in masked language models for multiple languages. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 274… [find](https://scholar.google.com/scholar?q=Masahiro+Kaneko%2C+Aizhan+Imankulova%2C+Danushka+Bollegala%2C+and+Naoaki+Okazaki%2E+2022%2E+Gender+bias+in+masked+language+models+for+multiple+languages%2E+In+Proceedings+of+the+2022+Conference+of+the+North+Ameri)
24. Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan. 2019. Learning from less data: A unified data subset selection and active learning framework for computer vision. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 12… [find](https://scholar.google.com/scholar?q=Vishal+Kaushal%2C+Rishabh+Iyer%2C+Suraj+Kothawade%2C+Rohan+Mahadev%2C+Khoshrav+Doctor%2C+and+Ganesh+Ramakrishnan%2E+2019%2E+Learning+from+less+data%3A+A+unified+data+subset+selection+and+active+learning+framework+for)
25. Cheng Li, Mengzhuo Chen, Jindong Wang, Sunayana Sitaram, and Xing Xie. 2024. [Culturellm: Incorporating cultural differences into large language models](https://md.research.elamur.ai/free/en/papers/li-2024-culturellm-incorporating-cultural-differences-large/). Advances in Neural Information Processing Systems, 37:84799–84838.
26. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016. [A diversity-promoting objective function for neural conversation models](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/).
27. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 110–119. [find](https://scholar.google.com/scholar?q=In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics%3A+Human+Language+Technologies%2C+pages+110%E2%80%93119%2E)
28. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15395–15406. [find](https://scholar.google.com/scholar?q=Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+pages+15395%E2%80%9315406%2E)
29. Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020. CommonGen: A constrained text generation challenge for generative commonsense reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1823–1840, Online. As… [find](https://scholar.google.com/scholar?q=Bill+Yuchen+Lin%2C+Wangchunshu+Zhou%2C+Ming+Shen%2C+Pei+Zhou%2C+Chandra+Bhagavatula%2C+Yejin+Choi%2C+and+Xiang+Ren%2E+2020%2E+CommonGen%3A+A+constrained+text+generation+challenge+for+generative+commonsense+reasoning%2E+I)
30. Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry. 2023. Trak: Attributing model behavior at scale. In International Conference on Machine Learning (ICML). [find](https://scholar.google.com/scholar?q=Sung+Min+Park%2C+Kristian+Georgiev%2C+Andrew+Ilyas%2C+Guillaume+Leclerc%2C+and+Aleksander+Madry%2E+2023%2E+Trak%3A+Attributing+model+behavior+at+scale%2E+In+International+Conference+on+Machine+Learning+%28ICML%29%2E)
31. Chenzhengyi Liu, Jie Huang, Kerui Zhu, and Kevin Chen-Chuan Chang. 2023. DimonGen: Diversified generative commonsense reasoning for explaining concept relationships. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pages 4719–4731, Toronto, Canada. Associat… [find](https://scholar.google.com/scholar?q=Chenzhengyi+Liu%2C+Jie+Huang%2C+Kerui+Zhu%2C+and+Kevin+Chen%2DChuan+Chang%2E+2023%2E+DimonGen%3A+Diversified+generative+commonsense+reasoning+for+explaining+concept+relationships%2E+In+Proceedings+of+the+61st+Annual+)
32. Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, and 1 others. 2024. [Best practices and lessons learned on synthetic data](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/). arXiv preprint arXiv:2404.07503.
33. Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022. [Generating training data with language models: Towards zero-shot language understanding](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/). Advances in Neural Information Processing Systems, 35:462–477.
34. Brando Miranda, Alycia Lee, Sudharsan Sundar, Allison Casasola, and Sanmi Koyejo. 2023. Beyond scale: The diversity coefficient as a data quality metric for variability in natural language data. arXiv preprint arXiv:2306.13840. — [arXiv:2306.13840](https://arxiv.org/abs/2306.13840) [find](https://scholar.google.com/scholar?q=Brando+Miranda%2C+Alycia+Lee%2C+Sudharsan+Sundar%2C+Allison+Casasola%2C+and+Sanmi+Koyejo%2E+2023%2E+Beyond+scale%3A+The+diversity+coefficient+as+a+data+quality+metric+for+variability+in+natural+language+data%2E+arXiv)
35. Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Associati… [find](https://scholar.google.com/scholar?q=Nasrin+Mostafazadeh%2C+Nathanael+Chambers%2C+Xiaodong+He%2C+Devi+Parikh%2C+Dhruv+Batra%2C+Lucy+Vanderwende%2C+Pushmeet+Kohli%2C+and+James+Allen%2E+2016%2E+A+corpus+and+cloze+evaluation+for+deeper+understanding+of+commo)
36. Ranjita Naik, Varun Chandrasekaran, Mert Yuksekgonul, Hamid Palangi, and Besmira Nushi. 2023. Diversity of thought improves reasoning abilities of llms. arXiv preprint arXiv:2310.07088. — [arXiv:2310.07088](https://arxiv.org/abs/2310.07088) [find](https://scholar.google.com/scholar?q=Ranjita+Naik%2C+Varun+Chandrasekaran%2C+Mert+Yuksekgonul%2C+Hamid+Palangi%2C+and+Besmira+Nushi%2E+2023%2E+Diversity+of+thought+improves+reasoning+abilities+of+llms%2E+arXiv+preprint+arXiv%3A2310%2E07088%2E+%E2%80%94+%5BarXiv%3A2310%2E)
37. Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. 2020. [CrowS-pairs: A challenge dataset for measuring social biases in masked language models](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1953–1967, Stroudsburg, PA, USA. As…
38. Xingyuan Pan, Luyang Huang, Liyan Kang, Zhicheng Liu, Yu Lu, and Shanbo Cheng. 2024. G-dig: Towards gradient-based diverse and high-quality instruction data selection for machine translation. In Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, and Xin… [find](https://scholar.google.com/scholar?q=Xingyuan+Pan%2C+Luyang+Huang%2C+Liyan+Kang%2C+Zhicheng+Liu%2C+Yu+Lu%2C+and+Shanbo+Cheng%2E+2024%2E+G%2Ddig%3A+Towards+gradient%2Dbased+diverse+and+high%2Dquality+instruction+data+selection+for+machine+translation%2E+In+Yulei)
39. Ozan Sener and Silvio Savarese. 2017. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489. [find](https://scholar.google.com/scholar?q=Ozan+Sener+and+Silvio+Savarese%2E+2017%2E+Active+learning+for+convolutional+neural+networks%3A+A+core%2Dset+approach%2E+arXiv+preprint+arXiv%3A1708%2E00489%2E)
40. Lin Shi, Chiyu Ma, Wenhua Liang, Xingjian Diao, Weicheng Ma, and Soroush Vosoughi. 2025. [Judging the judges: A systematic study of position bias in llm-as-a-judge](https://md.research.elamur.ai/free/en/papers/shi-2025-judging-judges-systematic-study-position/). In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chap…
41. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023. [The curse of recursion: Training on generated data makes models forget](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/). arXiv preprint arXiv:2305.17493.
42. Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI conference on artificial intelligence, volume 31. [find](https://scholar.google.com/scholar?q=Robyn+Speer%2C+Joshua+Chin%2C+and+Catherine+Havasi%2E+2017%2E+Conceptnet+5%2E5%3A+An+open+multilingual+graph+of+general+knowledge%2E+In+Proceedings+of+the+AAAI+conference+on+artificial+intelligence%2C+volume+31%2E)
43. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. [CommonsenseQA: A question answering challenge targeting commonsense knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
44. Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, and Jonathan Berant. 2021. [CommonsenseQA 2.0: Exposing the limits of AI through gamification](https://md.research.elamur.ai/free/en/papers/talmor-2022-commonsenseqa-2-0-exposing-limits/). In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).
45. Kushal Tirumala, Daniel Simig, Armen Aghajanyan, and Ari Morcos. 2023. [D4: Improving llm pretraining via document de-duplication and diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/). Advances in Neural Information Processing Systems, 36:53983–53995.
46. Cunxiang Wang, Shuailong Liang, Yili Jin, Yilong Wang, Xiaodan Zhu, and Yue Zhang. 2020. [Semeval-2020 task 4: Commonsense validation and explanation](https://md.research.elamur.ai/free/en/papers/wang-2020-semeval-2020-task-4-commonsense/). In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 307–321.
47. Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, and 1 others. 2024. Large language models are not fair evaluators. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, pages 9440–9450. [find](https://scholar.google.com/scholar?q=Peiyi+Wang%2C+Lei+Li%2C+Liang+Chen%2C+Zefan+Cai%2C+Dawei+Zhu%2C+Binghuai+Lin%2C+Yunbo+Cao%2C+Lingpeng+Kong%2C+Qi+Liu%2C+Tianyu+Liu%2C+and+1+others%2E+2024%2E+Large+language+models+are+not+fair+evaluators%2E+In+Proceedings+of+t)
48. Zaitian Wang, Jinghan Zhang, Xinhao Zhang, Kunpeng Liu, Pengfei Wang, and Yuanchun Zhou. 2025. [Diversity-oriented data augmentation with large language models](https://md.research.elamur.ai/free/en/papers/wang-2025-diversity-oriented-data-augmentation-large/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22265– 22283, Vien…
49. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824– 24837.
50. Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, and Alham Fikri Aji. 2024. [Lamini-lm: A diverse herd of distilled models from large-scale instructions](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/). In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)…
51. Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, and Tongliang Liu. 2022. Moderate coreset: A universal method of data selection for real-world data-efficient deep learning. In The Eleventh International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xiaobo+Xia%2C+Jiale+Liu%2C+Jun+Yu%2C+Xu+Shen%2C+Bo+Han%2C+and+Tongliang+Liu%2E+2022%2E+Moderate+coreset%3A+A+universal+method+of+data+selection+for+real%2Dworld+data%2Defficient+deep+learning%2E+In+The+Eleventh+Internation)
52. Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy S Liang. 2023. Data selection for language models via importance resampling. Advances in Neural Information Processing Systems, 36:34201– 34227. [find](https://scholar.google.com/scholar?q=Sang+Michael+Xie%2C+Shibani+Santurkar%2C+Tengyu+Ma%2C+and+Percy+S+Liang%2E+2023%2E+Data+selection+for+language+models+via+importance+resampling%2E+Advances+in+Neural+Information+Processing+Systems%2C+36%3A34201%E2%80%93+3422)
53. Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2024. [Metamath: Bootstrap your own mathematical questions for large language models](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/). In ICLR.
54. Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, and Meng Jiang. 2022. Diversifying content generation for commonsense reasoning with mixture of knowledge graph experts. In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022), pages… [find](https://scholar.google.com/scholar?q=Wenhao+Yu%2C+Chenguang+Zhu%2C+Lianhui+Qin%2C+Zhihan+Zhang%2C+Tong+Zhao%2C+and+Meng+Jiang%2E+2022%2E+Diversifying+content+generation+for+commonsense+reasoning+with+mixture+of+knowledge+graph+experts%2E+In+Proceedings+)
55. Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2023. [Large language model as attributed training data generator: A tale of diversity and bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/). Advances in neural information processing systems, 36:55734–55784.
56. Tianhui Zhang, Bei Peng, and Danushka Bollegala. 2024. Improving diversity of commonsense generation by large language models via in-context learning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9226–9242, Miami, Florida, USA. Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Tianhui+Zhang%2C+Bei+Peng%2C+and+Danushka+Bollegala%2E+2024%2E+Improving+diversity+of+commonsense+generation+by+large+language+models+via+in%2Dcontext+learning%2E+In+Findings+of+the+Association+for+Computational+)
57. Tianhui Zhang, Bei Peng, and Danushka Bollegala. 2025. Evaluating the evaluation of diversity in commonsense generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24258– 24275, Vienna, Austria. Association for Computatio… [find](https://scholar.google.com/scholar?q=Tianhui+Zhang%2C+Bei+Peng%2C+and+Danushka+Bollegala%2E+2025%2E+Evaluating+the+evaluation+of+diversity+in+commonsense+generation%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+the+Association+for+Computational+)
58. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, and 1 others. 2023. [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). arXiv preprint arXiv:2306.05685.
59. Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018. [Texygen: A benchmarking platform for text generation models](https://md.research.elamur.ai/free/en/papers/zhu-2018-texygen-benchmarking-platform-text-generation/). In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 1097–1100.
60. Yuchang Zhu, Huizhe Zhang, Bingzhe Wu, Jintang Li, Zibin Zheng, Peilin Zhao, Liang Chen, and Yatao Bian. 2025. Measuring diversity in synthetic datasets. In Forty-second International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Yuchang+Zhu%2C+Huizhe+Zhang%2C+Bingzhe+Wu%2C+Jintang+Li%2C+Zibin+Zheng%2C+Peilin+Zhao%2C+Liang+Chen%2C+and+Yatao+Bian%2E+2025%2E+Measuring+diversity+in+synthetic+datasets%2E+In+Forty%2Dsecond+International+Conference+on+Ma)

*Updated 2 November 2026*