This repo collect research papers about leveraging the capabilities of language models, which can be a good reference for building upper-layer applications.
- Model
- Prompt
- Information Seeking
- Action and Plan
- Reasoning
- Long-text Generation
- Analysis and Boundary
- Motivation
- Application
- Safety
- Related Project
❗ Here, only generative language models are included.
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Köpf, Andreas and Kilcher, Yannic and von Rütte, Dimitri and Anagnostidis, Sotiris et al., Alexander,OpenAssistant Conversations -- Democratizing Large Language Model Alignment,2023, [Paper]
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Zhao, Wayne Xin and Zhou, Kun and Li, Junyi et al.,A Survey of Large Language Models,2023, [Paper]
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Xu, Canwen and Guo, Daya and Duan, Nan and McAuley, Julian,Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data,2023, [Paper]
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Lin, Baihan and Bouneffouf, Djallel and Cecchi, Guillermo and Varshney, Kush R.,Towards Healthy AI: Large Language Models Need Therapists Too,2023, [Paper]
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Xiong, Honglin and Wang, Sheng and Zhu, Yitao and Zhao, Zihao and Liu, Yuxiao and Wang, Qian and Shen, Dinggang,DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task,2023, [Paper]
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Wu, Shijie and Irsoy, Ozan and Lu, Steven et al.,BloombergGPT: A Large Language Model for Finance,2023, [Paper]
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Bubeck, Sébastien and Chandrasekaran, Varun and Eldan, Ronen and Gehrke, Johannes and Horvitz, Eric and Kamar, Ece and Lee, Peter and Lee, Yin Tat and Li, Yuanzhi and Lundberg, Scott and Nori, Harsha and Palangi, Hamid and Ribeiro, Marco Tulio and Zhang, Yi, Sparks of Artificial General Intelligence: Early experiments with GPT-4, 2023 [Paper]
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OpenAI, GPT-4 Technical Report, 2023 [Paper]
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Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timothée and Rozière, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume, LLaMA: Open and Efficient Foundation Language Models, 2023 [Paper]
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Wei, Chengwei and Wang, Yun-Cheng and Wang, Bin and Kuo, C.-C. Jay, An Overview on Language Models: Recent Developments and Outlook, 2023 [Paper]
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Amatriain, Xavier, Transformer models: an introduction and catalog, 2023 [Paper]
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Zhou, Ce and Li, Qian and Li, Chen and Yu, Jun et al., A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT, 2023 [Paper]
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Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier et al., LLaMA: Open and Efficient Foundation Language Models, 2023 [Paper]
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Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V. and Zoph, Barret and Wei, Jason and Roberts, Adam, The Flan Collection: Designing Data and Methods for Effective Instruction Tuning, 2023 [Paper]
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Iyer, Srinivasan and Lin, Xi Victoria and Pasunuru, Ramakanth et al., OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization, 2023 [Paper]
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Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya, Improving Language Understanding by Generative Pre-Training, 2023 [Paper]
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Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya, Language Models are Unsupervised Multitask Learners, 2023 [Paper]
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Smith, Shaden and Patwary, Mostofa and Norick, Brandon et al., Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model, 2022 [Paper]
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Du, Nan and Huang, Yanping and Dai, Andrew M. and Tong, Simon et al., GLaM: Efficient Scaling of Language Models with Mixture-of-Experts, 2022 [Paper]
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Borgeaud, Sebastian and Mensch, Arthur and Hoffmann, Jordan et al., Improving language models by retrieving from trillions of tokens, 2022 [Paper]
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Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur et al., Training Compute-Optimal Large Language Models, 2022 [Paper]
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Workshop, BigScience and Scao, Teven Le, et al., BLOOM: A 176B-Parameter Open-Access Multilingual Language Model, 2022 [Paper]
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Bai, Yuntao and Jones, Andy and Ndousse, Kamal et al., Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, 2022 [Paper]
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Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo et al., Training language models to follow instructions with human feedback, 2022 [Paper]
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Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur et al., Training Compute-Optimal Large Language Models, 2022 [Paper]
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Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret et al., Scaling Instruction-Finetuned Language Models, 2022 [Paper]
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Scheurer, Jérémy and Campos, Jon Ander and Chan, Jun Shern and Chen, Angelica and Cho, Kyunghyun and Perez, Ethan, Training Language Models with Language Feedback, 2022 [Paper]
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Zhang, Susan and Roller, Stephen and Goyal, Naman et al., OPT: Open Pre-trained Transformer Language Models, 2022 [Paper]
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Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob et al., PaLM: Scaling Language Modeling with Pathways, 2022 [Paper]
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Bai, Yuntao and Kadavath, Saurav and Kundu, Sandipan et al., Constitutional AI: Harmlessness from AI Feedback, 2022 [Paper]
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Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming et al., Evaluating Large Language Models Trained on Code, 2021 [Paper]
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Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J., Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, 2020 [Paper]
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Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie et al., Language Models are Few-Shot Learners, 2020 [Paper]
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Lieber, Opher and Sharir, Or and Lentz, Barak and Shoham, Yoav, Jurassic-1: Technical Details and Evaluation, 2020 [Paper]
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Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension, 2019 [Paper]
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Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong,RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment,2023, [Paper]
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Scheurer, Jérémy and Campos, Jon Ander and Korbak, Tomasz and Chan, Jun Shern and Chen, Angelica and Cho, Kyunghyun and Perez, Ethan,Training Language Models with Language Feedback at Scale,2023, [Paper]
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Kwon, Minae and Xie, Sang Michael and Bullard, Kalesha and Sadigh, Dorsa, Reward Design with Language Models, 2023 [Paper]
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Go, Dongyoung and Korbak, Tomasz and Kruszewski, Germán and Rozen, Jos and Ryu, Nahyeon and Dymetman, Marc, Aligning Language Models with Preferences through f-divergence Minimization, 2023 [Paper]
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Myers, Vivek and Bıyık, Erdem and Sadigh, Dorsa, Active Reward Learning from Online Preferences, 2023 [Paper]
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Hartmann, Jochen and Schwenzow, Jasper and Witte, Maximilian, The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation, 2023 [Paper]
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Liu, Ruibo and Jia, Chenyan and Zhang, Ge and Zhuang, Ziyu and Liu, Tony X. and Vosoughi, Soroush, Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits, 2023 [Paper]
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Mechergui, Malek and Sreedharan, Sarath, Goal Alignment: A Human-Aware Account of Value Alignment Problem, 2023 [Paper]
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Ganguli, Deep and Lovitt, Liane and Kernion, Jackson et al., Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned, 2023 [Paper]
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Liu, Hao and Sferrazza, Carmelo and Abbeel, Pieter, Languages are Rewards: Hindsight Finetuning using Human Feedback, 2023 [Paper]
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Prabhumoye, Shrimai and Patwary, Mostofa and Shoeybi, Mohammad and Catanzaro, Bryan, Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models, 2023 [Paper]
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Ganguli, Deep and Askell, Amanda and Schiefer, Nicholas et al., The Capacity for Moral Self-Correction in Large Language Models, 2023 [Paper]
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Korbak, Tomasz and Shi, Kejian and Chen, Angelica and Bhalerao, Rasika and Buckley, Christopher L. and Phang, Jason and Bowman, Samuel R. and Perez, Ethan, Pretraining Language Models with Human Preferences, 2023 [Paper]
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Lee, Kimin and Liu, Hao and Ryu, Moonkyung and Watkins, Olivia and Du, Yuqing and Boutilier, Craig and Abbeel, Pieter and Ghavamzadeh, Mohammad and Gu, Shixiang Shane, Aligning Text-to-Image Models using Human Feedback, 2023 [Paper]
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Bakker, Michiel A. and Chadwick, Martin J. and Sheahan, Hannah R. and Tessler, Michael Henry and Campbell-Gillingham, Lucy and Balaguer, Jan and McAleese, Nat and Glaese, Amelia and Aslanides, John and Botvinick, Matthew M. and Summerfield, Christopher, Fine-tuning language models to find agreement among humans with diverse preferences, 2022 [Paper]
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Glaese, Amelia and McAleese, Nat and Trębacz, Maja et al., Improving alignment of dialogue agents via targeted human judgements, 2022 [Paper]
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Stiennon, Nisan and Ouyang, Long and Wu, Jeff and Ziegler, Daniel M. and Lowe, Ryan and Voss, Chelsea and Radford, Alec and Amodei, Dario and Christiano, Paul, Learning to summarize from human feedback, 2022 [Paper]
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Bai, Yuntao and Jones, Andy and Ndousse, Kamal et al., Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, 2022 [Paper]
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Bai, Yuntao and Kadavath, Saurav and Kundu, Sandipan et al., Constitutional AI: Harmlessness from AI Feedback, 2022 [Paper]
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Zhao, Lingjun and Nguyen, Khanh and Daumé III, Hal, A Cognitive Evaluation of Instruction Generation Agents tl;dr They Need Better Theory-of-Mind Capabilities, 2022 [Paper]
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Askell, Amanda and Bai, Yuntao and Chen, Anna et al., A General Language Assistant as a Laboratory for Alignment, 2021 [Paper]
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Thoppilan, Romal and De Freitas, Daniel and Hall, Jamie et al., LaMDA: Language Models for Dialog Applications, 2022 [Paper]
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Shuster, Kurt and Xu, Jing and Komeili, Mojtaba and Ju, Da et al., BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage, 2022 [Paper]
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Glaese, Amelia and McAleese, Nat and Trębacz, Maja et al., Improving alignment of dialogue agents via targeted human judgements, 2022 [Paper]
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Zhang, Yizhe and Sun, Siqi and Galley, Michel and Chen, Yen-Chun and Brockett, Chris and Gao, Xiang and Gao, Jianfeng and Liu, Jingjing and Dolan, Bill, DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation, 2020 [Paper]
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Zhang, Yizhe and Sun, Siqi and Galley, Michel and Chen, Yen-Chun and Brockett, Chris and Gao, Xiang and Gao, Jianfeng and Liu, Jingjing and Dolan, Bill, DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation, 2020 [Paper]
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Zhou, Yulin and Zhao, Yiren and Shumailov, Ilia and Mullins, Robert and Gal, Yarin,Revisiting Automated Prompting: Are We Actually Doing Better?,2023, [Paper]
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Liu, Chi and Wang, Haochun and Xi, Nuwa and Zhao, Sendong and Qin, Bing,Global Prompt Cell: A Portable Control Module for Effective Prompt,2023, [Paper]
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Pitis, Silviu and Zhang, Michael R. and Wang, Andrew and Ba, Jimmy,Boosted Prompt Ensembles for Large Language Models,2023, [Paper]
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Wu, Tongshuang and Jiang, Ellen and Donsbach, Aaron and Gray, Jeff and Molina, Alejandra and Terry, Michael and Cai, Carrie J.,PromptChainer: Chaining Large Language Model Prompts through Visual Programming,2022, [Paper]
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Chen, Jiuhai and Chen, Lichang and Huang, Heng and Zhou, Tianyi,When do you need Chain-of-Thought Prompting for ChatGPT?,2023, [Paper]
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Madaan, Aman and Yazdanbakhsh, Amir,Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango,2022, [Paper]
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Orhan, A. Emin,Recognition, recall, and retention of few-shot memories in large language models,2023, [Paper]
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Zhou, Wenxuan and Zhang, Sheng and Poon, Hoifung and Chen, Muhao, Context-faithful Prompting for Large Language Models, 2023 [Paper]
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Lyu, Xinxi and Min, Sewon and Beltagy, Iz and Zettlemoyer, Luke and Hajishirzi, Hannaneh, Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations, 2022 [Paper]
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Beurer-Kellner, Luca and Fischer, Marc and Vechev, Martin, Prompting Is Programming: A Query Language For Large Language Models, 2022 [Paper]
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Kim, Seungone and Joo, Se June and Jang, Yul and Chae, Hyungjoo and Yeo, Jinyoung, CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification, 2023 [Paper]
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Wei, Jerry and Wei, Jason and Tay, Yi and Tran, Dustin and Webson, Albert and Lu, Yifeng and Chen, Xinyun and Liu, Hanxiao and Huang, Da and Zhou, Denny and Ma, Tengyu, Larger language models do in-context learning differently, 2023 [Paper]
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Singh, Chandan and Morris, John X. and Aneja, Jyoti and Rush, Alexander M. and Gao, Jianfeng, Explaining Patterns in Data with Language Models via Interpretable Autoprompting, 2023 [Paper]
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Zhang, Renrui and Hu, Xiangfei and Li, Bohao and Huang, Siyuan and Deng, Hanqiu and Li, Hongsheng and Qiao, Yu and Gao, Peng, Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners, 2023 [Paper]
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Wu, Zhiyong and Wang, Yaoxiang and Ye, Jiacheng and Kong, Lingpeng, Self-adaptive In-context Learning, 2022 [Paper]
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Lu, Yao and Bartolo, Max and Moore, Alastair and Riedel, Sebastian and Stenetorp, Pontus, Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity, 2022 [Paper]
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Wu, Zhenyu and Wang, YaoXiang and Ye, Jiacheng and Feng, Jiangtao and Xu, Jingjing and Qiao, Yu and Wu, Zhiyong, OpenICL: An Open-Source Framework for In-context Learning, 2023 [Paper]
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Ye, Seonghyeon and Hwang, Hyeonbin and Yang, Sohee and Yun, Hyeongu and Kim, Yireun and Seo, Minjoon, In-Context Instruction Learning, 2023 [Paper]
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Liu, Hao and Sferrazza, Carmelo and Abbeel, Pieter, Chain of Hindsight Aligns Language Models with Feedback, 2023 [Paper]
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Shum, KaShun and Diao, Shizhe and Zhang, Tong, Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data, 2023 [Paper]
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Diao, Shizhe and Wang, Pengcheng and Lin, Yong and Zhang, Tong, Active Prompting with Chain-of-Thought for Large Language Models, 2023 [Paper]
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Li, Xiaonan and Qiu, Xipeng, Finding Supporting Examples for In-Context Learning, 2023 [Paper]
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Wang, Yunlong and Shen, Shuyuan and Lim, Brian Y., RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions, 2023 [Paper]
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Wei, Xiang and Cui, Xingyu and Cheng, Ning and Wang, Xiaobin and Zhang, Xin and Huang, Shen and Xie, Pengjun and Xu, Jinan and Chen, Yufeng and Zhang, Meishan and Jiang, Yong and Han, Wenjuan, Zero-Shot Information Extraction via Chatting with ChatGPT, 2023 [Paper]
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Li, Zekun and Peng, Baolin and He, Pengcheng and Galley, Michel and Gao, Jianfeng and Yan, Xifeng, Guiding Large Language Models via Directional Stimulus Prompting, 2023 [Paper]
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Diao, Shizhe and Wang, Pengcheng and Lin, Yong and Zhang, Tong, Active Prompting with Chain-of-Thought for Large Language Models, 2023 [Paper]
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Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex, Multimodal Chain-of-Thought Reasoning in Language Models, 2023 [Paper]
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Wen, Yuxin and Jain, Neel and Kirchenbauer, John and Goldblum, Micah and Geiping, Jonas and Goldstein, Tom, Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery, 2023 [Paper]
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Goswami, Koustava and Lange, Lukas and Araki, Jun and Adel, Heike, SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains, 2023 [Paper]
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Cheng, Zhoujun and Kasai, Jungo and Yu, Tao, Batch Prompting: Efficient Inference with Large Language Model APIs, 2023 [Paper]
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Lyu, Qing and Havaldar, Shreya and Stein, Adam and Zhang, Li and Rao, Delip and Wong, Eric and Apidianaki, Marianna and Callison-Burch, Chris, Faithful Chain-of-Thought Reasoning, 2023 [Paper]
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Shao, Zhihong and Gong, Yeyun and Shen, Yelong and Huang, Minlie and Duan, Nan and Chen, Weizhu, Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models, 2023 [Paper]
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Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh, Self-Instruct: Aligning Language Model with Self Generated Instructions, 2022 [Paper]
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Deng, Mingkai and Wang, Jianyu and Hsieh, Cheng-Ping and Wang, Yihan and Guo, Han and Shu, Tianmin and Song, Meng and Xing, Eric P. and Hu, Zhiting, RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning, 2022 [Paper]
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Khot, Tushar and Trivedi, Harsh and Finlayson, Matthew and Fu, Yao and Richardson, Kyle and Clark, Peter and Sabharwal, Ashish, Decomposed Prompting: A Modular Approach for Solving Complex Tasks, 2022 [Paper]
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Khot, Tushar and Richardson, Kyle and Khashabi, Daniel and Sabharwal, Ashish, Hey AI, Can You Solve Complex Tasks by Talking to Agents?, 2022 [Paper]
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Honovich, Or and Shaham, Uri and Bowman, Samuel R. and Levy, Omer, Instruction Induction: From Few Examples to Natural Language Task Descriptions, 2022 [Paper]
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Yuan, Weizhe and Liu, Pengfei, reStructured Pre-training, 2022 [Paper]
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Zhong, Wanjun and Gao, Yifan and Ding, Ning and Qin, Yujia and Liu, Zhiyuan and Zhou, Ming and Wang, Jiahai and Yin, Jian and Duan, Nan, ProQA: Structural Prompt-based Pre-training for Unified Question Answering, 2022 [Paper]
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Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Smola, Alex, Automatic Chain of Thought Prompting in Large Language Models, 2022 [Paper]
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Kojima, Takeshi and Gu, Shixiang Shane and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke, Large Language Models are Zero-Shot Reasoners, 2022 [Paper]
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Weng, Jinta and Hu, Yue and Tian, Zhihong and Huang, Heyan, ConsPrompt: Easily Exploiting Contrastive Samples for Few-shot Prompt Learning, 2022 [Paper]
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Wang, Xuezhi and Wei, Jason and Schuurmans, Dale and Le, Quoc and Chi, Ed and Narang, Sharan and Chowdhery, Aakanksha and Zhou, Denny, Self-Consistency Improves Chain of Thought Reasoning in Language Models, 2022 [Paper]
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Beurer-Kellner, Luca and Fischer, Marc and Vechev, Martin, Prompting Is Programming: A Query Language For Large Language Models, 2022 [Paper]
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Zhou, Yongchao and Muresanu, Andrei Ioan and Han, Ziwen and Paster, Keiran and Pitis, Silviu and Chan, Harris and Ba, Jimmy, Large Language Models Are Human-Level Prompt Engineers, 2022 [Paper]
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Anna-Maria Meck, Lisa Precht, How to Design the Perfect Prompt: A Linguistic Approach to Prompt Design in Automotive Voice Assistants – An Exploratory Study \textbar 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2022 [Paper]
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Arora, Simran and Narayan, Avanika and Chen, Mayee F. and Orr, Laurel and Guha, Neel and Bhatia, Kush and Chami, Ines and Sala, Frederic and Ré, Christopher, Ask Me Anything: A simple strategy for prompting language models, 2022 [Paper]
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Rubin, Ohad and Herzig, Jonathan and Berant, Jonathan, Learning To Retrieve Prompts for In-Context Learning, 2022 [Paper]
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Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, 2021 [Paper]
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Reynolds, Laria and McDonell, Kyle, Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm, 2021 [Paper]
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Liu, Jiachang and Shen, Dinghan and Zhang, Yizhe and Dolan, Bill and Carin, Lawrence and Chen, Weizhu, What Makes Good In-Context Examples for GPT-3?, 2021 [Paper]
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Perez, Ethan and Kiela, Douwe and Cho, Kyunghyun, True Few-Shot Learning with Language Models, 2021 [Paper]
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Gao, Tianyu and Fisch, Adam and Chen, Danqi, Making Pre-trained Language Models Better Few-shot Learners, 2021 [Paper]
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Han, Xu and Zhao, Weilin and Ding, Ning and Liu, Zhiyuan and Sun, Maosong, PTR: Prompt Tuning with Rules for Text Classification, 2021 [Paper]
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Taylor Shin, Yasaman Razeghi, Robert L. Logan, Eric Wallace, Sameer Singh, AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts, 2020 [Paper]
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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, Denny Zhou, Chain of Thought Prompting Elicits Reasoning in Large Language Models, 2020 [Paper]
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Lei, Tao and Bai, Junwen and Brahma, Siddhartha and Ainslie, Joshua and Lee, Kenton and Zhou, Yanqi and Du, Nan et al.,Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference,2023, [Paper]
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Hu, Zhiqiang and Lan, Yihuai and Wang, Lei and Xu, Wanyu and Lim, Ee-Peng and Lee, Roy Ka-Wei and Bing, Lidong and Poria, Soujanya,LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models,2023, [Paper]
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Zhang, Qingru and Chen, Minshuo and Bukharin, Alexander and He, Pengcheng and Cheng, Yu and Chen, Weizhu and Zhao, Tuo, Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning, 2023 [Paper]
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Chen, Derek and Lee, Celine and Lu, Yunan and Rosati, Domenic and Yu, Zhou, Mixture of Soft Prompts for Controllable Data Generation, 2023 [Paper]
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Yang, Xianjun and Cheng, Wei and Zhao, Xujiang and Petzold, Linda and Chen, Haifeng, Dynamic Prompting: A Unified Framework for Prompt Tuning, 2023 [Paper]
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Xiao, Guangxuan and Lin, Ji and Han, Song, Offsite-Tuning: Transfer Learning without Full Model, 2023 [Paper]
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Razdaibiedina, Anastasia and Mao, Yuning and Hou, Rui and Khabsa, Madian and Lewis, Mike and Almahairi, Amjad, Progressive Prompts: Continual Learning for Language Models, 2023 [Paper]
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Chen, Chen and Zhang, Wei Emma and Shakeri, Alireza Seyed, Incorporating Knowledge into Document Summarization: an Application of Prefix-Tuning on GPT-2, 2023 [Paper]
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Bowman, Benjamin and Achille, Alessandro and Zancato, Luca and Trager, Matthew and Perera, Pramuditha and Paolini, Giovanni and Soatto, Stefano, \textbackslash`A-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting, 2023 [Paper]
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Sun, Simeng and Liu, Yang and Iter, Dan and Zhu, Chenguang and Iyyer, Mohit, How Does In-Context Learning Help Prompt Tuning?, 2023 [Paper]
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Wang, Zifeng and Zhang, Zizhao and Ebrahimi, Sayna and Sun, Ruoxi and Zhang, Han and Lee, Chen-Yu and Ren, Xiaoqi and Su, Guolong and Perot, Vincent and Dy, Jennifer and Pfister, Tomas, DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning, 2022 [Paper]
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Nie, Xing and Ni, Bolin and Chang, Jianlong and Meng, Gaomeng and Huo, Chunlei and Zhang, Zhaoxiang and Xiang, Shiming and Tian, Qi and Pan, Chunhong, Pro-tuning: Unified Prompt Tuning for Vision Tasks, 2022 [Paper]
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Wang, Shijie and Chang, Jianlong and Wang, Zhihui and Li, Haojie and Ouyang, Wanli and Tian, Qi, Fine-grained Retrieval Prompt Tuning, 2022 [Paper]
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Santos, Cicero Nogueira dos and Dong, Zhe and Cer, Daniel and Nham, John and Shakeri, Siamak and Ni, Jianmo and Sung, Yun-hsuan, Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts, 2022 [Paper]
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Ma, Fang and Zhang, Chen and Ren, Lei and Wang, Jingang and Wang, Qifan and Wu, Wei and Quan, Xiaojun and Song, Dawei, XPrompt: Exploring the Extreme of Prompt Tuning, 2022 [Paper]
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Liu, Xiao and Ji, Kaixuan and Fu, Yicheng and Tam, Weng Lam and Du, Zhengxiao and Yang, Zhilin and Tang, Jie, P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks, 2022 [Paper]
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Cui, Ganqu and Hu, Shengding and Ding, Ning and Huang, Longtao and Liu, Zhiyuan, Prototypical Verbalizer for Prompt-based Few-shot Tuning, 2022 [Paper]
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He, Yun and Zheng, Huaixiu Steven and Tay, Yi and Gupta, Jai and Du, Yu and Aribandi, Vamsi and Zhao, Zhe and Li, YaGuang and Chen, Zhao and Metzler, Donald and Cheng, Heng-Tze and Chi, Ed H., HyperPrompt: Prompt-based Task-Conditioning of Transformers, 2022 [Paper]
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Gu, Yuxian and Han, Xu and Liu, Zhiyuan and Huang, Minlie, PPT: Pre-trained Prompt Tuning for Few-shot Learning, 2021 [Paper]
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Li, Xiang Lisa and Liang, Percy, Prefix-Tuning: Optimizing Continuous Prompts for Generation, 2021 [Paper]
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Logan IV, Robert L. and Balažević, Ivana and Wallace, Eric and Petroni, Fabio and Singh, Sameer and Riedel, Sebastian, Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models, 2021 [Paper]
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Peters, Matthew E. and Ruder, Sebastian and Smith, Noah A., To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks, 2019 [Paper]
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Madaan, Aman and Tandon, Niket and Clark, Peter and Yang, Yiming, Memory-assisted prompt editing to improve GPT-3 after deployment, 2023 [Paper]
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Chen, Maximillian and Papangelis, Alexandros and Tao, Chenyang and Kim, Seokhwan and Rosenbaum, Andy and Liu, Yang and Yu, Zhou and Hakkani-Tur, Dilek, PLACES: Prompting Language Models for Social Conversation Synthesis, 2023 [Paper]
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Kutela, Boniphace and Li, Shoujia and Das, Subasish and Liu, Jinli, ChatGPT as the Transportation Equity Information Source for Scientific Writing, 2023 [Paper]
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Liu, Zhengliang and Yu, Xiaowei and Zhang, Lu and Wu, Zihao and Cao, Chao and Dai, Haixing and Zhao, Lin and Liu, Wei and Shen, Dinggang and Li, Quanzheng and Liu, Tianming and Zhu, Dajiang and Li, Xiang, DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4, 2023 [Paper]
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Wang, Jiaan and Liang, Yunlong and Meng, Fandong and Li, Zhixu and Qu, Jianfeng and Zhou, Jie, Cross-Lingual Summarization via ChatGPT, 2023 [Paper]
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Imani, Shima and Du, Liang and Shrivastava, Harsh, Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification, 2023 [Paper]
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Di Bartolomeo, Sara and Severi, Giorgio and Schetinger, Victor and Dunne, Cody, Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms, 2023 [Paper]
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Burtell, Matthew and Woodside, Thomas, Artificial Influence: An Analysis Of AI-Driven Persuasion, 2023 [Paper]
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Prodan, George and Pelican, Elena, Prompt scoring system for dialogue summarization using GPT-3, 2022 [Paper]
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Kargaran, Amir Hossein and Nikeghbal, Nafiseh and Heydarnoori, Abbas and Schütze, Hinrich, MenuCraft: Interactive Menu System Design with Large Language Models, 2023 [Paper]
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Polak, Maciej P. and Morgan, Dane, Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering -- Example of ChatGPT, 2023 [Paper]
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Zhang, Dell and Schilder, Frank and Conrad, Jack G. and Makrehchi, Masoud and von Rickenbach, David and Moulinier, Isabelle, Making a Computational Attorney, 2023 [Paper]
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Imani, Shima and Du, Liang and Shrivastava, Harsh, MathPrompter: Mathematical Reasoning using Large Language Models, 2023 [Paper]
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Dai, Haixing and Liu, Zhengliang and Liao, Wenxiong and Huang, Xiaoke and Wu, Zihao and Zhao, Lin and Liu, Wei and Liu, Ninghao and Li, Sheng and Zhu, Dajiang and Cai, Hongmin and Li, Quanzheng and Shen, Dinggang and Liu, Tianming and Li, Xiang, ChatAug: Leveraging ChatGPT for Text Data Augmentation, 2023 [Paper]
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Qin, Chengwei and Zhang, Aston and Zhang, Zhuosheng and Chen, Jiaao and Yasunaga, Michihiro and Yang, Diyi, Is ChatGPT a General-Purpose Natural Language Processing Task Solver?, 2023 [Paper]
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Dai, Haixing and Liu, Zhengliang and Liao, Wenxiong and Huang, Xiaoke and Wu, Zihao and Zhao, Lin and Liu, Wei and Liu, Ninghao and Li, Sheng and Zhu, Dajiang and Cai, Hongmin and Li, Quanzheng and Shen, Dinggang and Liu, Tianming and Li, Xiang, ChatAug: Leveraging ChatGPT for Text Data Augmentation, 2023 [Paper]
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Brocki, Lennart and Dyer, George C. and Gładka, Anna and Chung, Neo Christopher, Deep Learning Mental Health Dialogue System, 2023 [Paper]
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Ott, Simon and Hebenstreit, Konstantin and Liévin, Valentin and Hother, Christoffer Egeberg and Moradi, Milad and Mayrhauser, Maximilian and Praas, Robert and Winther, Ole and Samwald, Matthias, ThoughtSource: A central hub for large language model reasoning data, 2023 [Paper]
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Nguyen, Ha-Thanh, A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3, 2023 [Paper]
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Argyle, Lisa P. and Busby, Ethan and Gubler, Joshua and Bail, Chris and Howe, Thomas and Rytting, Christopher and Wingate, David, AI Chat Assistants can Improve Conversations about Divisive Topics, 2023 [Paper]
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Kocaballi, A. Baki, Conversational AI-Powered Design: ChatGPT as Designer, User, and Product, 2023 [Paper]
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Shanahan, Murray, Talking About Large Language Models, 2023 [Paper]
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Shibata, Hisaichi and Miki, Soichiro and Nakamura, Yuta, Playing the Werewolf game with artificial intelligence for language understanding, 2023 [Paper]
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Megahed, Fadel M. and Chen, Ying-Ju and Ferris, Joshua A. and Knoth, Sven and Jones-Farmer, L. Allison, How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study, 2023 [Paper]
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Lehman, Eric and Hernandez, Evan and Mahajan, Diwakar and Wulff, Jonas and Smith, Micah J. and Ziegler, Zachary and Nadler, Daniel and Szolovits, Peter and Johnson, Alistair and Alsentzer, Emily, Do We Still Need Clinical Language Models?, 2023 [Paper]
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Zeng, Donghuo and Wu, Jianming and Wang, Yanan and Matsumoto, Kazunori and Hattori, Gen and Ikeda, Kazushi, Topic-switch adapted Japanese Dialogue System based on PLATO-2, 2023 [Paper]
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Feng, Yutao and Qiang, Jipeng and Li, Yun and Yuan, Yunhao and Zhu, Yi, Sentence Simplification via Large Language Models, 2023 [Paper]
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Noever, David and McKee, Forrest, Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals, 2022 [Paper]
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Haolan Zhan, Yufei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Gholamreza Haffari, Let's Negotiate! A Survey of Negotiation Dialogue Systems, 2022 [Paper]
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Li, Haoran and Guo, Dadi and Fan, Wei and Xu, Mingshi and Song, Yangqiu,Multi-step Jailbreaking Privacy Attacks on ChatGPT,2023, [Paper]
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Raman, Mrigank and Maini, Pratyush and Kolter, J. Zico and Lipton, Zachary C. and Pruthi, Danish, Model-tuning Via Prompts Makes NLP Models Adversarially Robust, 2023 [Paper]
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Greshake, Kai and Abdelnabi, Sahar and Mishra, Shailesh and Endres, Christoph and Holz, Thorsten and Fritz, Mario, More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models, 2023 [Paper]
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Kang, Daniel and Li, Xuechen and Stoica, Ion and Guestrin, Carlos and Zaharia, Matei and Hashimoto, Tatsunori, Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks, 2023 [Paper]