Fetching the paper…
Reading the bibliography…
The paradigm of retrieval-augmented generated (RAG) helps mitigate hallucinations of large language models (LLMs).
Xor qa: Cross-lingual open-retrieval question answering
Akari Asai, Jungo Kasai, Jonathan H Clark, Kenton Lee, Eunsol Choi, and Hannaneh Hajishirzi. 2020 · 2010
Earlier work this paper cites.
Cross-lingual information retrieval
Nurul Amelina Nasharuddin and Muhamad Taufik Abdullah. 2010 · 2010
Earlier work this paper cites.
Book review: Cross-language information retrieval by jian-yun nie
Marcello Federico. 2011 · 2011
Earlier work this paper cites.
Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings
Ivan Vulić and Marie-Francine Moens. 2015 · 2015
Earlier work this paper cites.
Tydi qa: A benchmark for information-seeking question answering in typologically diverse languages
Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 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, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
One question answering model for many languages with cross-lingual dense passage retrieval
Akari Asai, Xinyan Yu, Jungo Kasai, and Hanna Hajishirzi. 2021 · 2021
Earlier work this paper cites.
Mia 2022 shared task: Evaluating cross-lingual open-retrieval question answering for 16 diverse languages
Akari Asai, Shayne Longpre, Jungo Kasai, Chia-Hsuan Lee, Rui Zhang, Junjie Hu, Ikuya Yamada, Jonathan H Clark, and Eunsol Choi. 2022 · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Mega: Multilingual evaluation of generative ai
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, et al. 2023 · 2023
Cited alongside, same era.
Neural approaches to multilingual information retrieval
Dawn Lawrie, Eugene Yang, Douglas W Oard, and James Mayfield. 2023 · 2023
Cited alongside, same era.
When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
Cross-lingual consistency of factual knowledge in multilingual language models
Jirui Qi, Raquel Fernández, and Arianna Bisazza. 2023 · 2023
Cited alongside, same era.
Retrieval-augmented generation in multilingual settings
Nadezhda Chirkova, David Rau, Hervé Déjean, Thibault Formal, Stéphane Clinchant, and Vassilina Nikoulina. 2024 · 2024
Closest in time.
C4ai command r+ 08-2024
Cohere For AI. 2024 · 2024
Closest in time.
This land is your, my land: Evaluating geopolitical bias in language models through territorial disputes
Bryan Li, Samar Haider, and Chris Callison-Burch. 2024b · 2024
Closest in time.
Llama Team. 2024 · 2024
Closest in time.
New embedding models and api updates
OpenAI. 2024b · 2024
Closest in time.
Faux polyglot: A study on information disparity in multilingual large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages
Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, and Jimmy Lin. 2023 · 2023
Cited alongside, same era.
Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy. 2024 · 2024
Cited alongside, same era.
Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024a
Cited in the paper.
Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024b
Cited in the paper.
Eliciting better multilingual structured reasoning from LLMs through code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman, Nikolaos Pappas, and Saab Mansour. 2024a
Cited in the paper.
Uncovering differences in persuasive language in russian versus english wikipedia
Bryan Li, Aleksey Panasyuk, and Chris Callison-Burch. 2024c
Cited in the paper.
Nikhil Sharma, Kenton Murray, and Ziang Xiao. 2024 · 2024
Closest in time.
Nomiracl: Knowing when you don’t know for robust multilingual retrieval-augmented generation
Nandan Thakur, Luiz Bonifacio, Xinyu Zhang, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Boxing Chen, Mehdi Rezagholizadeh, and Jimmy Lin. 2024 · 2024
Closest in time.
Not all languages are equal: Insights into multilingual retrieval-augmented generation
Suhang Wu, Jialong Tang, Baosong Yang, Ante Wang, Kaidi Jia, Jiawei Yu, Junfeng Yao, and Jinsong Su. 2024 · 2024
Closest in time.