Fetching the paper…
Reading the bibliography…
While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks.
Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
Earlier work this paper cites.
Paraphrase augmented task-oriented dialog generation
Silin Gao, Yichi Zhang, Zhijian Ou, and Zhou Yu. 2020 · 2004
Earlier work this paper cites.
Interactive and interpretable machine learning models for human machine collaboration
Been Kim. 2015 · 2015
Earlier work this paper cites.
A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Earlier work this paper cites.
Guided open vocabulary image captioning with constrained beam search
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017 · 2017
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and T. Jaakkola. 2018 · 2018
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Earlier work this paper cites.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton. 2018 · 2018
Earlier work this paper cites.
Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Matt Post and David Vilar. 2018 · 2018
Earlier work this paper cites.
ParaNMT-50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
John Wieting and Kevin Gimpel. 2018 · 2018
Earlier work this paper cites.
A multi-task approach for disentangling syntax and semantics in sentence representations
Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdel rahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Earlier work this paper cites.
Exploring diverse expressions for paraphrase generation
Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, and Yong Yu. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
The multilingual Amazon reviews corpus
Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. 2020 · 2020
Earlier work this paper cites.
Syntax-guided controlled generation of paraphrases
A. Kumar, Kabir Ahuja, Raghuram Vadapalli, and P. Talukdar. 2020 · 2020
Cited alongside, same era.
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020 · 2020
Cited alongside, same era.
Controlling style in generated dialogue
Eric Michael Smith, Diana Gonzalez-Rico, Emily Dinan, and Y-Lan Boureau. 2020 · 2020
Cited alongside, same era.
Explanations for CommonsenseQA: New Dataset and Models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
Cited alongside, same era.
SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
Investigating the benefits of free-form rationales
Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022 · 2022
Later among the works it cites.
Zero-shot sonnet generation with discourse-level planning and aesthetics features
Yufei Tian and Nanyun Peng. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2022 · 2022
Later among the works it cites.
Teaching algorithmic reasoning via in-context learning
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, and Hanie Sedghi. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kuan-Hao Huang and Kai-Wei Chang. 2021 · 2021
Cited alongside, same era.
NeuroLogic decoding: (un)supervised neural text generation with predicate logic constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
Cited alongside, same era.
Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Cited alongside, same era.
AESOP: Paraphrase generation with adaptive syntactic control
Jiao Sun, Xuezhe Ma, and Nanyun Peng. 2021 · 2021
Cited alongside, same era.
Topic-controlled text generation
Cansen Çağlayan and Murat Karakaya. 2021 · 2021
Cited alongside, same era.
Towards robust NLG bias evaluation with syntactically-diverse prompts
Arshiya Aggarwal, Jiao Sun, and Nanyun Peng. 2022 · 2022
Cited alongside, same era.
Truncation sampling as language model desmoothing
John Hewitt, Christopher Manning, and Percy Liang. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Mega: Multilingual evaluation of generative ai
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Maxamed Axmed, Kalika Bali, and Sunayana Sitaram. 2023 · 2023
Closest in time.
Falcon-40B: an open large language model with state-of-the-art performance
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023 · 2023
Closest in time.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
Closest in time.
Exploring the feasibility of chatgpt for event extraction
Jun Gao, Huan Zhao, Changlong Yu, and Ruifeng Xu. 2023 · 2023
Closest in time.
Do models really learn to follow instructions? an empirical study of instruction tuning
Po-Nien Kung and Nanyun Peng. 2023 · 2023
Closest in time.
A systematic study and comprehensive evaluation of chatgpt on benchmark datasets
Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq R. Joty, and J. Huang. 2023 · 2023
Closest in time.
Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
Closest in time.
Language model acceptability judgements are not always robust to context
Koustuv Sinha, Jon Gauthier, Aaron Mueller, Kanishka Misra, Keren Fuentes, Roger Levy, and Adina Williams. 2023 · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Closest in time.
Unsupervised melody-to-lyrics generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, and Nanyun Peng. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Closest in time.
Did you read the instructions? rethinking the effectiveness of task definitions in instruction learning
Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, and Chien-Sheng Jason Wu. 2023 · 2023
Closest in time.
Controlled text generation with natural language instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, and Mrinmaya Sachan. 2023 · 2023
Closest in time.