Fetching the paper…
Reading the bibliography…
This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states.
The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
Earlier work this paper cites.
Matrix analysis
Roger A Horn and Charles R Johnson. 2012 · 2012
Earlier work this paper cites.
Machine learning: a probabilistic perspective
Kevin P Murphy. 2012 · 2012
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 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.
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 · 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, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Earlier work this paper cites.
IIRC: A dataset of incomplete information reading comprehension questions
James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, and Pradeep Dasigi. 2020 · 2020
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Earlier work this paper cites.
Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019 · 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 · 2020
Earlier work this paper cites.
Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020 · 2020
Earlier work this paper cites.
Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark Gales. 2020 · 2021
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
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. 2022 · 2022
Cited alongside, same era.
Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
Cited alongside, same era.
Syntactically robust training on partially-observed data for open information extraction
Ji Qi, Yuxiang Chen, Lei Hou, Juanzi Li, and Bin Xu. 2022 · 2022
Cited alongside, same era.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Later among the works it cites.
Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Self-knowledge guided retrieval augmentation for large language models
Yile Wang, Peng Li, Maosong Sun, and Yang Liu. 2023 · 2023
Later among the works it cites.
Adaptive chameleon or stubborn sloth: Unraveling the behavior of large language models in knowledge conflicts
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 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 · 2023
Cited alongside, same era.
Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
The internal state of an llm knows when its lying
Amos Azaria and Tom Mitchell. 2023 · 2023
Cited alongside, same era.
Probabilistic tree-of-thought reasoning for answering knowledge-intensive complex questions
Shulin Cao, Jiajie Zhang, Jiaxin Shi, Xin Lv, Zijun Yao, Qi Tian, Lei Hou, and Juanzi Li. 2023 · 2023
Cited alongside, same era.
Hallucination detection: Robustly discerning reliable answers in large language models
Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, and Yanghua Xiao. 2023b · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023 · 2023
Later among the works it cites.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2023
Later among the works it cites.
On the calibration of large language models and alignment
Chiwei Zhu, Benfeng Xu, Quan Wang, Yongdong Zhang, and Zhendong Mao. 2023 · 2023
Later among the works it cites.
Representation engineering: A top-down approach to ai transparency
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. 2023 · 2023
Later among the works it cites.
Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park. 2024 · 2024
Closest in time.
Untangle the KNOT: Interweaving conflicting knowledge and reasoning skills in large language models
Yantao Liu, Zijun Yao, Xin Lv, Yuchen Fan, Shulin Cao, Jifan Yu, Lei Hou, and Juanzi Li. 2024 · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date
Meta. 2024 · 2024
Closest in time.
Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024 · 2024
Closest in time.
Keheng Wang, Feiyu Duan, Peiguang Li, Sirui Wang, and Xunliang Cai. 2024 · 2024
Closest in time.
Perils of self-feedback: Self-bias amplifies in large language models
Wenda Xu, Guanglei Zhu, Xuandong Zhao, Liangming Pan, Lei Li, and William Yang Wang. 2024 · 2024
Closest in time.
Xunjian Yin, Xu Zhang, Jie Ruan, and Xiaojun Wan. 2024 · 2024
Closest in time.
Can large language models faithfully express their intrinsic uncertainty in words?
Gal Yona, Roee Aharoni, and Mor Geva. 2024 · 2024
Closest in time.
Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, and Jie Tang. 2024 · 2024
Closest in time.