Fetching the paper…
Reading the bibliography…
Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness.
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.
Syntactic Structures
Noam Chomsky. 1957 · 1957
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Earlier work this paper cites.
Visualizing and understanding neural models in nlp
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2015 · 2015
Earlier work this paper cites.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Earlier work this paper cites.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres. 2018 · 2018
Earlier work this paper cites.
Deep learning for case-based reasoning through prototypes: a neural network that explains its predictions
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2018 · 2018
Earlier work this paper cites.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
Earlier work this paper cites.
Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
Earlier work this paper cites.
Sanvis: Visual analytics for understanding self-attention networks
Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, Jaehyo Yoo, Bum Chul Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, and Jaegul Choo. 2019 · 2019
Earlier work this paper cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Earlier work this paper cites.
A multiscale visualization of attention in the transformer model
Jesse Vig. 2019 · 2019
Earlier work this paper cites.
Thread: Circuits
Nick Cammarata, Shan Carter, Gabriel Goh, Chris Olah, Michael Petrov, Ludwig Schubert, Chelsea Voss, Ben Egan, and Swee Kiat Lim. 2020 · 2020
Cited alongside, same era.
spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
Cited alongside, same era.
A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2021 · 2021
Cited alongside, same era.
Towards a unified game-theoretic view of adversarial perturbations and robustness
Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, and Quanshi Zhang. 2021 · 2021
DecompX: Explaining transformers decisions by propagating token decomposition
Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh, Yadollah Yaghoobzadeh, and Mohammad Taher Pilehvar. 2023 · 2023
Later among the works it cites.
Quantifying context mixing in transformers
Hosein Mohebbi, Willem Zuidema, Grzegorz Chrupała, and Afra Alishahi. 2023 · 2023
Later among the works it cites.
Defining and quantifying the emergence of sparse concepts in dnns
Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, and Quanshi Zhang. 2023 · 2023
Later among the works it cites.
SlimPajama: A 627B token cleaned and deduplicated version of RedPajama
Daria Soboleva, Faisal Al-Khateeb, Robert Myers, Jacob R Steeves, Joel Hestness, and Nolan Dey. 2023 · 2023
Later among the works it cites.
Internlm: A multilingual language model with progressively enhanced capabilities
InternLM Team. 2023 · 2023
Later among the works it cites.
Craft: Concept recursive activation factorization for explainability
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Enolp musk@SMM4H’22 : Leveraging pre-trained language models for stance and premise classification
Millon Das, Archit Mangrulkar, Ishan Manchanda, Manav Kapadnis, and Sohan Patnaik. 2022 · 2022
Cited alongside, same era.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
Cited alongside, same era.
Probing GPT-3’s linguistic knowledge on semantic tasks
Lining Zhang, Mengchen Wang, Liben Chen, and Wenxin Zhang. 2022 · 2022
Cited alongside, same era.
Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal, Sravan Bodapati, Katrin Kirchhoff, and Dan Roth. 2023 · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Cited alongside, same era.
Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
Cited alongside, same era.
Fel Thomas, Picard Agustin, Bethune Louis, Boissin Thibaut, Vigouroux David, Colin Julien, Cadène Rémi, and Serre Thomas. 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.
Interpretability in the wild: a circuit for indirect object identification in GPT-2 small
Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. 2023 · 2023
Later among the works it cites.
Local interpretation of transformer based on linear decomposition
Sen Yang, Shujian Huang, Wei Zou, Jianbing Zhang, Xinyu Dai, and Jiajun Chen. 2023 · 2023
Later among the works it cites.
Towards the difficulty for a deep neural network to learn concepts of different complexities
Dongrui Liu, Huiqi Deng, Xu Cheng, Qihan Ren, Kangrui Wang, and Quanshi Zhang. 2024 · 2024
Closest in time.
Towards tracing trustworthiness dynamics: Revisiting pre-training period of large language models
Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, and Jing Shao. 2024 · 2024
Closest in time.
Exploring safety generalization challenges of large language models via code
Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, and Lizhuang Ma. 2024 · 2024
Closest in time.
Attentionviz: A global view of transformer attention
Catherine Yeh, Yida Chen, Aoyu Wu, Cynthia Chen, Fernanda Viégas, and Martin Wattenberg. 2024 · 2024
Closest in time.
Explaining generalization power of a dnn using interactive concepts
Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, and Quanshi Zhang. 2024 · 2024
Closest in time.