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Steerability, or the ability of large language models (LLMs) to adapt outputs to align with diverse community-specific norms, perspectives, and communication styles, is critical for real-world applications but remains under-evaluated.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Steering large language models for machine translation with finetuning and in-context learning
Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, and Andre Martins. 2023 · 2023
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Anger breeds controversy: analyzing controversy and emotions on reddit
Kai Chen, Zihao He, Rong-Ching Chang, Jonathan May, and Kristina Lerman. 2023 · 2023
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SteerLM: Attribute conditioned SFT as an (user-steerable) alternative to RLHF
Yi Dong, Zhilin Wang, Makesh Sreedhar, Xianchao Wu, and Oleksii Kuchaiev. 2023 · 2023
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Evaluating large language models in generating synthetic hci research data: a case study
Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023 · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
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Role play with large language models
Murray Shanahan, Kyle McDonell, and Laria Reynolds. 2023 · 2023
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Character-LLM: A trainable agent for role-playing
Yunfan Shao, Linyang Li, Junqi Dai, and Xipeng Qiu. 2023 · 2023
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and 1 others. 2023 · 2023
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Instruction-following evaluation for large language models
Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023 · 2023
Cited alongside, same era.
Personalized steering of large language models: Versatile steering vectors through bi-directional preference optimization
Yuanpu Cao, Tianrong Zhang, Bochuan Cao, Ziyi Yin, Lu Lin, Fenglong Ma, and Jinghui Chen. 2024 · 2024
Cited alongside, same era.
How susceptible are large language models to ideological manipulation?
Kai Chen, Zihao He, Jun Yan, Taiwei Shi, and Kristina Lerman. 2024a · 2024
Cited alongside, same era.
Language models for text classification: Is in-context learning enough?
Aleksandra Edwards and Jose Camacho-Collados. 2024 · 2024
Cited alongside, same era.
Socially aware synthetic data generation for suicidal ideation detection using large language models
Hamideh Ghanadian, Isar Nejadgholi, and Hussein Al Osman. 2024 · 2024
CultureBank: An online community-driven knowledge base towards culturally aware language technologies
Weiyan Shi, Ryan Li, Yutong Zhang, Caleb Ziems, Sunny Yu, Raya Horesh, Rogério Abreu De Paula, and Diyi Yang. 2024b · 2024
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Conifer: Improving complex constrained instruction-following ability of large language models
Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang, Baohua Dong, Ran Lin, and Ruohui Huang. 2024 · 2024
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Let me speak freely? a study on the impact of format restrictions on large language model performance
Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen. 2024 · 2024
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RoleLLM: Benchmarking, eliciting, and enhancing role-playing abilities of large language models
Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, and Junran Peng. 2024a · 2024
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CodecLM: Aligning language models with tailored synthetic data
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Cited alongside, same era.
Steering large language models for cross-lingual information retrieval
Ping Guo, Yubing Ren, Yue Hu, Yanan Cao, Yunpeng Li, and Heyan Huang. 2024 · 2024
Cited alongside, same era.
Community-cross-instruct: Unsupervised instruction generation for aligning large language models to online communities
Zihao He, Minh Duc Chu, Rebecca Dorn, Siyi Guo, and Kristina Lerman. 2024b · 2024
Cited alongside, same era.
Whose emotions and moral sentiments do language models reflect?
Zihao He, Siyi Guo, Ashwin Rao, and Kristina Lerman. 2024c · 2024
Cited alongside, same era.
The steerability of large language models toward data-driven personas
Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, and Rahul Gupta. 2024b · 2024
Cited alongside, same era.
Large language model instruction following: A survey of progresses and challenges
Renze Lou, Kai Zhang, and Wenpeng Yin. 2024 · 2024
Cited alongside, same era.
Large language models are superpositions of all characters: Attaining arbitrary role-play via self-alignment
Keming Lu, Bowen Yu, Chang Zhou, and Jingren Zhou. 2024 · 2024
Cited alongside, same era.
Capturing minds, not just words: Enhancing role-playing language models with personality-indicative data
Yiting Ran, Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Yanghua Xiao, and Deqing Yang. 2024 · 2024
Cited alongside, same era.
Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, and Tomas Pfister. 2024b · 2024
Later among the works it cites.
WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang. 2024 · 2024
Later among the works it cites.
Evaluating large language models at evaluating instruction following
Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, and Danqi Chen. 2024 · 2024
Later among the works it cites.
Cfbench: A comprehensive constraints-following benchmark for llms
Tao Zhang, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, and 1 others. 2024 · 2024
Later among the works it cites.
Steering large language model activations in sparse spaces
Reza Bayat, Ali Rahimi-Kalahroudi, Mohammad Pezeshki, Sarath Chandar, and Pascal Vincent. 2025 · 2025
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ComPO: Community preferences for language model personalization
Sachin Kumar, Chan Young Park, Yulia Tsvetkov, Noah A. Smith, and Hannaneh Hajishirzi. 2025 · 2025
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Ziyi Liu, Priyanka Dey, Zhenyu Zhao, Jen-tse Huang, Rahul Gupta, Yang Liu, and Jieyu Zhao. 2025 · 2025
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Is in-context learning sufficient for instruction following in LLMs?
Hao Zhao, Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion. 2025 · 2025
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SyntheT2C: Generating synthetic data for fine-tuning large language models on the Text2Cypher task
Zijie Zhong, Linqing Zhong, Zhaoze Sun, Qingyun Jin, Zengchang Qin, and Xiaofan Zhang. 2025 · 2025
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