Fetching the paper…
Reading the bibliography…
In many practical applications, large language models (LLMs) need to acquire new knowledge not present in their pre-training data.
Okapi at trec-3
Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al · 1995
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Earlier work this paper cites.
On the efficacy of knowledge distillation
Jang Hyun Cho and Bharath Hariharan · 2019
Earlier work this paper cites.
Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2019
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 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.
The effect of natural distribution shift on question answering models
John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt · 2020
Earlier work this paper cites.
Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh · 2020
Earlier work this paper cites.
Xtremedistil: Multi-stage distillation for massive multilingual models
Subhabrata Mukherjee and Ahmed Awadallah · 2020
Earlier work this paper cites.
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou · 2020
Earlier work this paper cites.
A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
Cited alongside, same era.
Improving language models by retrieving from trillions of tokens
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
Cited alongside, same era.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
Later among the works it cites.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
Later among the works it cites.
Stanford alpaca: An instruction-following llama model, 2023
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
Later among the works it cites.
Instructretro: Instruction tuning post retrieval-augmented pretraining
Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, and Bryan Catanzaro · 2023
Later among the works it cites.
Qilin-med: Multi-stage knowledge injection advanced medical large language model
Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, Yining Hua, and Andrew Liu · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
Cited alongside, same era.
Prompt injection: Parameterization of fixed inputs
Eunbi Choi, Yongrae Jo, Joel Jang, and Minjoon Seo · 2022
Cited alongside, same era.
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi · 2022
Cited alongside, same era.
Learning by distilling context
Charlie Snell, Dan Klein, and Ruiqi Zhong · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Adapting language models to compress contexts
Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
Cited alongside, same era.
The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
Cited alongside, same era.
Later among the works it cites.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
Later among the works it cites.
Claude 3.5 sonnet model card addendum, 2024
Anthropic · 2024
Closest in time.
Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Closest in time.
Rag vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture
Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al · 2024
Closest in time.
T \ \backslash " ulu 3: Pushing frontiers in open language model post-training
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al · 2024
Closest in time.
Fine-tuning llama for multi-stage text retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin · 2024
Closest in time.
Injecting new knowledge into large language models via supervised fine-tuning
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, et al · 2024
Closest in time.
Learning to generate instruction tuning datasets for zero-shot task adaptation
Nihal V Nayak, Yiyang Nan, Avi Trost, and Stephen H Bach · 2024
Closest in time.
In-context editing: Learning knowledge from self-induced distributions
Siyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, and Zilong Zheng · 2024
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2024
Closest in time.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
Closest in time.
Raft: Adapting language model to domain specific rag
Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez · 2024
Closest in time.
Context compression and extraction: Efficiency inference of large language models
Junyao Zhou, Ruiqing Du, Yushan Tan, Jintao Yang, Zonghao Yang, Wei Luo, Zhunchen Luo, Xian Zhou, and Wenpeng Hu · 2024
Closest in time.