Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
Earlier work this paper cites.
Pointer sentinel mixture models. In International Conference on Learning Representations
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Earlier work this paper cites.
Memory-augmented Neural Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Yang Feng, Shiyue Zhang, Andi Zhang, Dong Wang, and Andrew Abel. 2017 · 2017
Earlier work this paper cites.
Improving neural language models with a continuous cache. In International Conference on Learning Representations
Edouard Grave, Armand Joulin, and Nicolas Usunier. 2017 · 2017
Earlier work this paper cites.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. 2017 · 2017
Earlier work this paper cites.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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. 2018 · 2018
Earlier work this paper cites.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
A convergence theory for deep learning via over-parameterization. In International Conference on Machine Learning . PMLR, 242–252
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 2019 · 2019
Earlier work this paper cites.
Simple, scalable adaptation for neural machine translation
Ankur Bapna, Naveen Arivazhagan, and Orhan Firat. 2019 · 2019
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning . PMLR, 2790–2799
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Earlier work this paper cites.
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2019 · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 2019
Earlier work this paper cites.
LEGAL-BERT: The Muppets straight out of Law School. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 2898–2904
Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020 · 2020
Earlier work this paper cites.
Recall and learn: Fine-tuning deep pretrained language models with less forgetting
Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu. 2020 · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
AdapterFusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych. 2020a · 2020
Earlier work this paper cites.
Adapterhub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020b · 2020
Earlier work this paper cites.
Mad-x: An adapter-based framework for multi-task cross-lingual transfer
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. 2020c · 2020
Earlier work this paper cites.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Earlier work this paper cites.
How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
Earlier work this paper cites.
Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 2020
Earlier work this paper cites.
Customizing contextualized language models for legal document reviews. In 2020 IEEE International Conference on Big Data (Big Data) . IEEE, 2139–2148
Shohreh Shaghaghian, Luna Yue Feng, Borna Jafarpour, and Nicolai Pogrebnyakov. 2020 · 2020
Earlier work this paper cites.
K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Guihong Cao, Daxin Jiang, Ming Zhou, et al · 2020
Earlier work this paper cites.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Xiang Chen, Ningyu Zhang, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
Earlier work this paper cites.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2021 · 2021
Earlier work this paper cites.
Editing Factual Knowledge in Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
Earlier work this paper cites.
WARP: Word-level Adversarial ReProgramming. In Annual Meeting of the Association for Computational Linguistics
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
Earlier work this paper cites.
Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder. 2021 · 2021
Earlier work this paper cites.
The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 3045–3059
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Earlier work this paper cites.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Earlier work this paper cites.
Unipelt: A unified framework for parameter-efficient language model tuning
Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen-tau Yih, and Madian Khabsa. 2021 · 2021
Earlier work this paper cites.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, and Dan Roth. 2021 · 2021
Earlier work this paper cites.
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2021 · 2021
Earlier work this paper cites.
CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models. In NeurIPS Efficient Natural Language and Speech Processing Workshop
Aashiq Muhamed, Iman Keivanloo, Sujan Perera, James Mracek, Yi Xu, Qingjun Cui, Santosh Rajagopalan, Belinda Zeng, and Trishul Chilimbi. 2021 · 2021
Earlier work this paper cites.
Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
Earlier work this paper cites.
LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of t5
Chengwei Qin and Shafiq Joty. 2021 · 2021
Earlier work this paper cites.
End-to-end training of multi-document reader and retriever for open-domain question answering
Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, and Dani Yogatama. 2021 · 2021
Earlier work this paper cites.
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Matthew Cer. 2021 · 2021
Earlier work this paper cites.
Raise a child in large language model: Towards effective and generalizable fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021 · 2021
Earlier work this paper cites.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg. 2021 · 2021
Earlier work this paper cites.
Aston Zhang, Yi Tay, Shuai Zhang, Alvin Chan, Anh Tuan Luu, Siu Cheung Hui, and Jie Fu. 2021a · 2021
Earlier work this paper cites.
Unsupervised domain adaptation with adapter
Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao, and Minlie Huang. 2021b · 2021
Earlier work this paper cites.
Attempt: Parameter-efficient multi-task tuning via attentional mixtures of soft prompts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . 6655–6672
Akari Asai, Mohammadreza Salehi, Matthew E Peters, and Hannaneh Hajishirzi. 2022 · 2022
Earlier work this paper cites.
Explicit Knowledge Transfer for Weakly-Supervised Code Generation
Zhangir Azerbayev, Ansong Ni, Hailey Schoelkopf, and Dragomir Radev. 2022 · 2022
Earlier work this paper cites.
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2022 · 2022
Earlier work this paper cites.
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding. In Conference on Empirical Methods in Natural Language Processing
Rishabh Bhardwaj, Amrita Saha, and Steven C. H. Hoi. 2022 · 2022
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens. In International conference on machine learning . 2206–2240
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
Earlier work this paper cites.
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning
Lucas Caccia, Edoardo Ponti, Lucas Liu, Matheus Pereira, Nicolas Le Roux, and Alessandro Sordoni. 2022 · 2022
Earlier work this paper cites.
Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards. In Conference on Empirical Methods in Natural Language Processing
Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. 2022 · 2022
Earlier work this paper cites.
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2022 · 2022
Earlier work this paper cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Earlier work this paper cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
Cited alongside, same era.
Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang. 2022 · 2022
Cited alongside, same era.
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning. In Conference on Empirical Methods in Natural Language Processing
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu. 2022 · 2022
Cited alongside, same era.
Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2022
Cited alongside, same era.
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Haojie Zhang, Ge Li, Jia Li, Zhongjin Zhang, Yuqi Zhu, and Zhi Jin. 2022a · 2022
Later among the works it cites.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022b · 2022
Later among the works it cites.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Hongyu Zhao, Hao Tan, and Hongyuan Mei. 2022 · 2022
Later among the works it cites.
Ask Me Anything: A simple strategy for prompting language models. In The Eleventh International Conference on Learning Representations
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, and Christopher Re. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Successive Prompting for Decomposing Complex Questions. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. 2022 · 2022
Cited alongside, same era.
KronA: Parameter Efficient Tuning with Kronecker Adapter
Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J Clark, and Mehdi Rezagholizadeh. 2022 · 2022
Cited alongside, same era.
P { \{ \ \backslash O } \} DA: Prompt-driven Zero-shot Domain Adaptation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, and Raoul de Charette. 2022 · 2022
Cited alongside, same era.
PAL: Program-aided Language Models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
Domain Adaptation via Prompt Learning
Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, and Gao Huang. 2022 · 2022
Cited alongside, same era.
PPT: Pre-trained Prompt Tuning for Few-shot Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Dublin, Ireland, 8410–8423
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022 · 2022
Cited alongside, same era.
Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation. In Findings of the Association for Computational Linguistics: EMNLP 2022 . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates
Xu Guo, Boyang Li, and Han Yu. 2022 · 2022
Cited alongside, same era.
On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey
Xu Guo and Han Yu. 2022 · 2022
Cited alongside, same era.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al · 2023
Closest in time.
Potential Use of Chat GPT in Global Warming
Som S Biswas. 2023 · 2023
Closest in time.
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S Yu, and Lichao Sun. 2023 · 2023
Closest in time.
Binding Language Models in Symbolic Languages. In The Eleventh International Conference on Learning Representations
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023 · 2023
Closest in time.
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models
Alexandra Chronopoulou, Matthew E Peters, Alexander Fraser, and Jesse Dodge. 2023 · 2023
Closest in time.
A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Joyce Ho, et al · 2023
Closest in time.
Collaborating with language models for embodied reasoning. In Second Workshop on Language and Reinforcement Learning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, and Rob Fergus. 2023 · 2023
Closest in time.
Compositional Semantic Parsing with Large Language Models. In The Eleventh International Conference on Learning Representations
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2023 · 2023
Closest in time.
Measuring and Manipulating Knowledge Representations in Language Models
Evan Hernandez, Belinda Z Li, and Jacob Andreas. 2023 · 2023
Closest in time.
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
Closest in time.
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Zhiqiang Hu, Yihuai Lan, Lei Wang, Wanyu Xu, Ee-Peng Lim, Roy Ka-Wei Lee, Lidong Bing, and Soujanya Poria. 2023 · 2023
Closest in time.
Language is not all you need: Aligning perception with language models
Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, et al · 2023
Closest in time.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
Closest in time.
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. In The Eleventh International Conference on Learning Representations
Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothee Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, and Yuhuai Wu. 2023 · 2023
Closest in time.
Do We Still Need Clinical Language Models?
Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair Johnson, and Emily Alsentzer. 2023 · 2023
Closest in time.
Sentiment Spin: Attacking Financial Sentiment with GPT-3
Markus Leippold. 2023 · 2023
Closest in time.
CAMEL: Communicative Agents for" Mind" Exploration of Large Scale Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023a · 2023
Closest in time.
Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, et al · 2023
Closest in time.
TaskMatrix. AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang Mao, et al · 2023
Closest in time.
Summary of chatgpt/gpt-4 research and perspective towards the future of large language models
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al · 2023
Closest in time.
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
Alejandro Lopez-Lira and Yuehua Tang. 2023 · 2023
Closest in time.
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. In 46th International ACM SIGIR Conference on Research and Development in Information Retrieval - Short Paper
Jiaying Lu, Jiaming Shen, Bo Xiong, Wengjing Ma, Staab Steffen, and Carl Yang. 2023 · 2023
Closest in time.
Designing Chemical Reaction Arrays using phactor and ChatGPT
Babak Mahjour, Jillian Hoffstadt, and Tim Cernak. 2023 · 2023
Closest in time.
UDApter–Efficient Domain Adaptation Using Adapters
Bhavitvya Malik, Abhinav Ramesh Kashyap, Min-Yen Kan, and Soujanya Poria. 2023 · 2023
Closest in time.
Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, and Thomas L Griffiths. 2023 · 2023
Closest in time.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al · 2023
Closest in time.
ChatGPT: Can artificial intelligence language models be of value for cardiovascular nurses and allied health professionals
Philip Moons and Liesbet Van Bulck. 2023 · 2023
Closest in time.
Improving Multiparty Interactions with a Robot Using Large Language Models. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems . 1–8
Prasanth Murali, Ian Steenstra, Hye Sun Yun, Ameneh Shamekhi, and Timothy Bickmore. 2023 · 2023
Closest in time.
ChatGPT plugins
OpenAI. [n. d.] · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023 · 2023
Closest in time.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
Closest in time.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Closest in time.
Tool Learning with Foundation Models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, et al · 2023
Closest in time.
In-Context Retrieval-Augmented Language Models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Closest in time.
Evaluating ChatGPT as an adjunct for radiologic decision-making
Arya Rao, John Kim, Meghana Kamineni, Michael Pang, Winston Lie, and Marc D Succi. 2023 · 2023
Closest in time.
Progressive Prompts: Continual Learning for Language Models. In The Eleventh International Conference on Learning Representations
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi. 2023 · 2023
Closest in time.
The utility of ChatGPT as an example of large language models in healthcare education, research and practice: Systematic review on the future perspectives and potential limitations
Malik Sallam. 2023 · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
Closest in time.
Memory Augmented Large Language Models are Computationally Universal
Dale Schuurmans. 2023 · 2023
Closest in time.
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023 · 2023
Closest in time.
Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick. 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
Closest in time.
On the Role of Negative Precedent in Legal Outcome Prediction
Josef Valvoda, Ryan Cotterell, and Simone Teufel. 2023 · 2023
Closest in time.
Document-level machine translation with large language models
Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023b · 2023
Closest in time.
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. 2023a · 2023
Closest in time.
ChatGPT Gets Its “Wolfram Superpowers”!
Stephen Wolfram. [n. d.] · 2023
Closest in time.
Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023 · 2023
Closest in time.
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023b · 2023
Closest in time.
Large language models can rate news outlet credibility
Kai-Cheng Yang and Filippo Menczer. 2023 · 2023
Closest in time.
Dynamic Prompting: A Unified Framework for Prompt Tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao, Linda Petzold, and Haifeng Chen. 2023a · 2023
Closest in time.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
Chaoning Zhang, Chenshuang Zhang, Sheng Zheng, Yu Qiao, Chenghao Li, Mengchun Zhang, Sumit Kumar Dam, Chu Myaet Thwal, Ye Lin Tun, Le Luang Huy, et al · 2023
Closest in time.
Llama-adapter: Efficient fine-tuning of language models with zero-init attention
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. 2023a · 2023
Closest in time.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Closest in time.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on Learning Representations
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
Closest in time.
ChatGPT and environmental research
Jun-Jie Zhu, Jinyue Jiang, Meiqi Yang, and Zhiyong Jason Ren. 2023 · 2023
Closest in time.