Evaluating Large Language Models on Controlled Generation Tasks
Open accessAbstract
Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Models can struggle at doing constrained generation with fine-grained constraints.
“And models can struggle at doing constrained generation with fine-grained constraints [23].”
From Generalizing Verifiable Instruction Following (2025) · cites this paper as [23]
Following instructions precisely and adhering to specific output or formatting constraints is often still a challenge for language models.
“Following instructions precisely and adhering to specific output or formatting constraints is often still a challenge for language models.”
From Generalizing Verifiable Instruction Following (2025) · cites this paper as [23]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · see citing works
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine
Jiao, W., Wang, W., Huang, J.-T., Wang, X., Shi, S., & Tu, Z. (2023) · arXiv · doi:10.48550/arxiv.2301.08745 · Cited by 317 · see citing works
Flagged first, then by citations
Research works printed but not linked: 44
- Arshiya Aggarwal, Jiao Sun, and Nanyun Peng. 2022. Towards robust NLG bias evaluation with syntactically-diverse prompts. In Findings of the As- sociation for Computational Linguistics: EMNLP 2022, pages 6022-6032, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Shourya Aggarwal, Divyanshu Mandowara, Vishwa- jeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021. Explanations for Common- senseQA: New Dataset and Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3050-3065, Online. Association for Computational Linguistics. · DOI
- David Alvarez-Melis and T. Jaakkola. 2018. Towards robust interpretability with self-explaining neural net- works. In NeurIPS.
- Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017. Guided open vocabulary im- age captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 936- 945, Copenhagen, Denmark. Association for Compu- tational Linguistics. · DOI
- Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 2019. A multi-task approach for dis- entangling syntax and semantics in sentence represen- tations. pages 2453-2464, Minneapolis, Minnesota. Association for Computational Linguistics.
- Hyung Won Chung, Le Hou, Shayne Longpre, Bar- ret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022b. Scaling instruction-finetuned language mod- els. arXiv preprint arXiv:2210.11416. · arXiv
- Silin Gao, Yichi Zhang, Zhijian Ou, and Zhou Yu. 2020. Paraphrase augmented task-oriented dialog genera- tion. ArXiv, abs/2004.07462. · arXiv
- Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple contrastive learning of sentence em- beddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing, pages 6894-6910, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics. · DOI · Paper page
- John Hewitt, Christopher Manning, and Percy Liang. 2022. Truncation sampling as language model desmoothing. In Findings of the Association for Com- putational Linguistics: EMNLP 2022, pages 3414- 3427, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Kuan-Hao Huang and Kai-Wei Chang. 2021. Generat- ing syntactically controlled paraphrases without us- ing annotated parallel pairs. ArXiv, abs/2101.10579. · arXiv
- Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018. Adversarial example generation with syntactically controlled paraphrase networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1875-1885, New Or- leans, Louisiana. Association for Computational Lin- guistics. · DOI
- Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. 2020. The multilingual Amazon reviews cor- pus. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4563-4568, Online. Association for Computational Linguistics. · DOI
- A. Kumar, Kabir Ahuja, Raghuram Vadapalli, and P. Talukdar. 2020. Syntax-guided controlled genera- tion of paraphrases. Transactions of the Association for Computational Linguistics, 8:330-345.
- Po-Nien Kung and Nanyun Peng. 2023. Do models really learn to follow instructions? an empirical study of instruction tuning. ACL 2023.
- Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022a. Contrastive de- coding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097. · arXiv
- Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto. 2022b. Diffusion- LM improves controllable text generation. In Ad- vances in Neural Information Processing Systems.
- Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020. CommonGen: A constrained text gen- eration challenge for generative commonsense rea- soning. In Findings of the Association for Computa- tional Linguistics: EMNLP 2020, pages 1823-1840, Online. Association for Computational Linguistics. · DOI · Paper page
- Zachary C Lipton. 2018. The mythos of model inter- pretability: In machine learning, the concept of in- terpretability is both important and slippery. Queue, 16(3):31-57.
- Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021. Neuro- Logic decoding: (un)supervised neural text genera- tion with predicate logic constraints. In Proceedings of the 2021 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 4288-4299, Online. Association for Computational Linguistics. · DOI
- Clara Meister, Tiago Pimentel, Gian Wiher, and Ryan Cotterell. 2022. Typical decoding for natural lan- guage generation. arXiv preprint arXiv:2202.00666. · arXiv
- Tao Meng, Sidi Lu, Nanyun Peng, and Kai-Wei Chang. 2022. Controllable text generation with neurally- decomposed oracle. In Advances in Neural Informa- tion Processing Systems, volume 35, pages 28125- 28139.
- 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 Association for Computational Linguistics: Human Language Technologies, pages 839-849, San Diego, California. Association for Computational Linguis- tics. · DOI · Paper page
- Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap be- tween neural text and human text using divergence frontiers. Advances in Neural Information Process- ing Systems, 34:4816-4828.
- Matt Post and David Vilar. 2018. Fast lexically con- strained decoding with dynamic beam allocation for neural machine translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume 1 (Long Pa- pers), pages 1314-1324, New Orleans, Louisiana. Association for Computational Linguistics. · DOI
- Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, and Yong Yu. 2019. Exploring diverse expressions for paraphrase generation. In Proceedings of the 2019 Conference on Empirical Methods in Natu- ral Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3173-3182, Hong Kong, China. Association for Computational Linguistics. · DOI
- Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi. 2022. COLD decoding: Energy-based con- strained text generation with langevin dynamics. In Advances in Neural Information Processing Systems.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain your- self! leveraging language models for commonsense reasoning. In Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019).
- Machel Reid, Victor Zhong, Suchin Gururangan, and Luke Zettlemoyer. 2022. M2D2: A massively multi- domain language modeling dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 964-975, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
- Koustuv Sinha, Jon Gauthier, Aaron Mueller, Kan- ishka Misra, Keren Fuentes, Roger Levy, and Adina Williams. 2023. Language model acceptability judge- ments are not always robust to context. In Proceed- ings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6043-6063, Toronto, Canada. Association for Computational Linguistics. · DOI
- Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Ling- peng Kong, and Nigel Collier. 2022. A contrastive framework for neural text generation. arXiv preprint arXiv:2202.06417. · arXiv
- Yixuan Su and Jialu Xu. 2022. An empirical study on contrastive search and contrastive decoding for open-ended text generation. arXiv preprint arXiv:2211.10797. · arXiv
- Jiao Sun, Xuezhe Ma, and Nanyun Peng. 2021. AESOP: Paraphrase generation with adaptive syntactic control. In Proceedings of the 2021 Conference on Empiri- cal Methods in Natural Language Processing, pages 5176-5189, Online and Punta Cana, Dominican Re- public. Association for Computational Linguistics. · DOI
- Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022. Investigating the benefits of free- form rationales. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5867-5882, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics.
- Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, and Nanyun Peng. 2023. Unsupervised melody-to-lyrics generation. In Proceedings of the 61st Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9235-9254, Toronto, Canada. Association for Computational Linguistics. · DOI
- Yufei Tian and Nanyun Peng. 2022. Zero-shot sonnet generation with discourse-level planning and aesthet- ics features. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, pages 3587-3597, Seattle, United States. Association for Computational Linguistics. · DOI
- Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and effi- cient foundation language models. arXiv preprint arXiv:2302.13971. · arXiv
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning in large language models. CoRR, abs/2201.11903. · arXiv
- Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Di- nan, Kyunghyun Cho, and Jason Weston. 2019. Neu- ral text generation with unlikelihood training. arXiv preprint arXiv:1908.04319. · arXiv
- John Wieting and Kevin Gimpel. 2018. ParaNMT-50M: Pushing the limits of paraphrastic sentence embed- dings with millions of machine translations. In Pro- ceedings of the 56th Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers), pages 451-462, Melbourne, Australia. As- sociation for Computational Linguistics. · DOI
- Nan Xu, Chunting Zhou, Asli Celikyilmaz, and Xuezhe Ma. 2023b. Look-back decoding for open-ended text generation. arXiv preprint arXiv:2305.13477. · arXiv
- Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caim- ing Xiong, and Chien-Sheng Jason Wu. 2023. Did you read the instructions? rethinking the effective- ness of task definitions in instruction learning. ACL 2023.
- Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2022. A survey of controllable text generation using transformer-based pre-trained language models. ArXiv, abs/2201.05337. · arXiv
- Cansen Çaglayan and Murat Karakaya. 2021. Topic- controlled text generation. In 2021 6th International Conference on Computer Science and Engineering (UBMK), pages 533-536. · DOI
Cited by
Sorted by citations
Writer-Defined AI Personas for On-Demand Feedback Generation
Benharrak, K., Zindulka, T., Lehmann, F., Heuer, H., & Buschek, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642406 · Cited by 63 · see citing works
Generalizing Verifiable Instruction Following
Pyatkin, V., Malik, S., Graf, V., Ivison, H., Huang, S., Dasigi, P., Lambert, N., & Hajishirzi, H. (2025) · arXiv · doi:10.48550/arxiv.2507.02833 · Cited by 4 · see citing works
Palmeira Ferraz, T., Mehta, K., Lin, Y.-H., Chang, H.-S., Oraby, S., Liu, S., Subramanian, V., Chung, T., Bansal, M., & Peng, N. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.458 · Cited by 3 · see citing works
Self-citation
- Authors
- Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J., Peng, N., & Ma, X.
- Affiliations
- University of Southern California (US)
- University of California, Los Angeles (US)
- ETH Zurich (CH)
- Amazon (United States) (US)
- Google DeepMind (United Kingdom) (GB)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 3155-3168
- OpenAlex
- W4389523912
- Cited by
- 29
- References
- 7
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 12
- FWCI
- 4.60
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- large language models, controlled generation, evaluation metrics, text generation, controllable generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
