---
title: "Who cites Tal August — 167 papers of our library"
description: "167 papers of the Elamur Research library cite the works of Tal August, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/tal-august/cited-by/"
---
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# Who cites Tal August

167 citations of their works from 167 papers of our library (of the 149,439 whose bibliographies we read) · 36 with a page here, the most cited first · [← Tal August](https://md.research.elamur.ai/free/en/authors/tal-august/)

1. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
2. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,440 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
3. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
4. **[ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 578 citations*
5. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation arXiv:2201.05955**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 317 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
6. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
7. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers arXiv:2102.01454**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 251 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
8. **[Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.*2023 · 242 citations*
9. [**Aligning Large Language Models with Human: A Survey arXiv:2307.12966**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
10. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
11. **[On the Risk of Misinformation Pollution with Large Language Models](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 179 citations*
12. **[Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 170 citations*
13. **[Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://md.research.elamur.ai/free/en/papers/dou-2022-is-gpt-3-text-indistinguishable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, et al.*2022 · 152 citations*
14. **[Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Liangming Pan, Michael Saxon, Wenda Xu, et al.*2024 · 140 citations*
15. **[Humans or LLMs as the Judge? A Study on Judgement Bias](https://md.research.elamur.ai/free/en/papers/chen-2024-humans-or-llms-as-judge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.*2024 · 133 citations*
16. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
17. **[LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization](https://md.research.elamur.ai/free/en/papers/krishna-2023-longeval-guidelines-human-evaluation-faithfulness/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kalpesh Krishna, Erin Bransom, Bailey Kuehl, et al.*2023 · 114 citations*
18. **[RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://md.research.elamur.ai/free/en/papers/dugan-2024-raid-shared-benchmark-robust-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liam Dugan, Alyssa Hwang, Filip Trhlík, et al.*2024 · 110 citations*
19. **[Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*
20. **[PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference](https://md.research.elamur.ai/free/en/papers/ji-2025-pku-saferlhf-multi-level-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaming Ji, Donghai Hong, Borong Zhang, et al.*2025 · 83 citations*
21. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 81 citations*
22. [**Humans or LLMs as the Judge? A Study on Judgement Biases arXiv:2402.10669**Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.*2024 · 78 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-humans-llms-judge-study-judgement/)
23. [**Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model**Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-chatgpt-wugs-morphology/)
24. **[Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation](https://md.research.elamur.ai/free/en/papers/chhun-2024-do-language-models-enjoy-their/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Cyril Chhun, Fabian M. Suchanek, Chloé Clavel*2024 · 41 citations*
25. **[M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework](https://md.research.elamur.ai/free/en/papers/chia-2025-m-longdoc-benchmark-multimodal-super/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yew Ken Chia, Liying Cheng, Hou Pong Chan, et al.*2025 · 21 citations*
26. **[The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://md.research.elamur.ai/free/en/papers/loakman-2023-iron-ic-melting-pot-reviewing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tyler Loakman, Aaron Maladry, Chenghua Lin*2023 · 19 citations*
27. [**Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges arXiv:2309.12426**Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury, et al.*2023 · 16 citations*](https://md.research.elamur.ai/free/en/papers/samuel-2023-llms-augment-low-resource-reading/)
28. **[Natural Language Processing RELIES on Linguistics](https://md.research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Juri Opitz, Shira Wein, Nathan Schneider*2025 · 13 citations*
29. **[CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation](https://md.research.elamur.ai/free/en/papers/ma-2026-clase-hybrid-method-chinese-legalese/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie*2026 · 1 citation*
30. **[Identifying Bias in Machine-generated Text Detection](https://md.research.elamur.ai/free/en/papers/stowe-2026-identifying-bias-machine-generated-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Stowe, Svetlana Afanaseva, Rodolfo C. Raimundo, et al.*2026 · 1 citation*
31. **[Text-to-Text Automatic Story Generation: A Survey](https://md.research.elamur.ai/free/en/papers/ma-2026-text-text-automatic-story-generation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuan Ma, Richard Susilo, Patrik Haslum, et al.*2026 · 1 citation*
32. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
33. **[When Translations Surprise: Human Awareness of Predictability in Translations](https://md.research.elamur.ai/free/en/papers/romero-2026-when-translations-surprise-human-awareness/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sanchez-Martinez*2026 · 1 citation*
34. **[Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation](https://md.research.elamur.ai/free/en/papers/teng-2026-beyond-static-artifacts-evolutionary-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yeqing Teng, Jiasheng Si, Shuxia Lin, et al.*2026*
35. **[Corpus and Baselines for Distinguishing Authentic, AI-Generated, and AI-Enhanced Resumes](https://md.research.elamur.ai/free/en/papers/loizidou-2026-corpus-baselines-distinguishing-authentic-ai/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Andrea Loizidou, Anshu Kiran Sharma, Adrian Esquivel, et al.*2026*
36. **[From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication](https://md.research.elamur.ai/free/en/papers/mondal-2026-fluent-useful-generative-ai-that/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ishani Mondal*2026*

131 more citing papers of our library have no page here yet.

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