Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation

The probabilistic relevance framework: BM25 and beyond

Stephen Robertson, Hugo Zaragoza, et al · 2009

Earlier work this paper cites.

HotpotQA: A dataset for diverse, explainable multi-hop question answering

Original

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018

Earlier work this paper cites.

Natural questions: a benchmark for question answering research

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019

Earlier work this paper cites.

Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps

Original

Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020

Earlier work this paper cites.

Leveraging passage retrieval with generative models for open domain question answering

Original

Gautier Izacard and Edouard Grave. 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.

KILT: a benchmark for knowledge intensive language tasks

Original

Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, et al · 2020

Earlier work this paper cites.

The power of scale for parameter-efficient prompt tuning

Original

Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021

Earlier work this paper cites.

P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks

Original

Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021

Earlier work this paper cites.

Improving language models by retrieving from trillions of tokens. In International conference on machine learning . PMLR, 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.

MuSiQue: Multihop Questions via Single-hop Question Composition

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022

Earlier work this paper cites.

Text Embeddings by Weakly-Supervised Contrastive Pre-training

Original

Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022

Earlier work this paper cites.

React: Synergizing reasoning and acting in language models

Original

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022

Earlier work this paper cites.

Retrieval-augmented multimodal language modeling

Original

Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2022 · 2022

Earlier work this paper cites.

Self-rag: Learning to retrieve, generate, and critique through self-reflection

Original

Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023 · 2023

Earlier work this paper cites.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Original

Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. 2023 · 2023

Earlier work this paper cites.

Longlora: Efficient fine-tuning of long-context large language models

Original

Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia. 2023 · 2023

Cited alongside, same era.

Adapting language models to compress contexts

Original

Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen. 2023 · 2023

Cited alongside, same era.

RedPajama: an Open Dataset for Training Large Language Models

Together Computer. 2023 · 2023

Cited alongside, same era.

Parameter-efficient fine-tuning of large-scale 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 · 2023

Cited alongside, same era.

Retrieval-augmented generation for large language models: A survey

Original

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023

C-Pack: Packaged Resources To Advance General Chinese Embedding

Original

Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023

Later among the works it cites.

Recomp: Improving retrieval-augmented lms with compression and selective augmentation

Original

Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023b · 2023

Later among the works it cites.

Retrieval meets long context large language models

Original

Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro. 2023a · 2023

Later among the works it cites.

Making retrieval-augmented language models robust to irrelevant context

Original

Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2023 · 2023

Later among the works it cites.