Fetching the paper…
Reading the bibliography…
Developers using LLMs and LLM-based agents in their applications have provided plenty of anecdotal evidence that in-context-learning (ICL) is fragile.
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 · 1901
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
N Reimers. 2019 · 1908
Earlier work this paper cites.
Question answering in webclopedia
Eduard H Hovy, Laurie Gerber, Ulf Hermjakob, Michael Junk, and Chin-Yew Lin. 2000 · 2000
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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.
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.
Meta-learning via language model in-context tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He. 2021 · 2021
Earlier work this paper cites.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Earlier work this paper cites.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Earlier work this paper cites.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
An information-theoretic approach to prompt engineering without ground truth labels
Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jeffrey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate. 2022 · 2022
Cited alongside, same era.
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2022 · 2022
Cited alongside, same era.
Coverage-based example selection for in-context learning
Shivanshu Gupta, Matt Gardner, and Sameer Singh. 2023 · 2023
Cited alongside, same era.
Granite: Scaling language models with ibm’s efficient architecture
Compositional exemplars for in-context learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2023 · 2023
Later among the works it cites.
What makes good examples for visual in-context learning?
Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu. 2023 · 2023
Later among the works it cites.
Rishabh Agarwal, Avi Singh, Lei M Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, et al. 2024 · 2024
Later among the works it cites.
One size doesn’t fit all: Predicting the number of examples for in-context learning
Manish Chandra, Debasis Ganguly, and Iadh Ounis. 2024 · 2024
Later among the works it cites.
What makes a good order of examples in in-context learning
Qi Guo, Leiyu Wang, Yidong Wang, Wei Ye, and Shikun Zhang. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
IBM. 2023 · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Cited alongside, same era.
Mixtral: A diverse and scalable instruction-tuned language model
MistralAI. 2023 · 2023
Cited alongside, same era.
Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. 2023 · 2023
Cited alongside, same era.
Restgpt: Connecting large language models with real-world restful apis
Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, et al. 2023 · 2023
Cited alongside, same era.
Llama 3: 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
Cited alongside, same era.
Representative demonstration selection for in-context learning with two-stage determinantal point process
Zhao Yang, Yuanzhe Zhang, Dianbo Sui, Cao Liu, Jun Zhao, and Kang Liu. 2023 · 2023
Cited alongside, same era.
Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024b
Cited in the paper.
Haoyu Liu, Jianfeng Liu, Shaohan Huang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Furu Wei, and Qi Zhang. 2024a · 2024
Later among the works it cites.
Prompt optimization via adversarial in-context learning
Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, and Junxian He. 2024 · 2024
Later among the works it cites.
Explora: Efficient exemplar subset selection for complex reasoning
Kiran Purohit, Raghuram Devalla, Krishna Mohan Yerragorla, Sourangshu Bhattacharya, Avishek Anand, et al. 2024 · 2024
Later among the works it cites.
In-context example ordering guided by label distributions
Zhichao Xu, Daniel Cohen, Bei Wang, and Vivek Srikumar. 2024 · 2024
Later among the works it cites.
Batch-icl: Effective, efficient, and order-agnostic in-context learning
Kaiyi Zhang, Ang Lv, Yuhan Chen, Hansen Ha, Tao Xu, and Rui Yan. 2024 · 2024
Later among the works it cites.
Is in-context learning sufficient for instruction following in llms?
Hao Zhao, Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion. 2024 · 2024
Later among the works it cites.