Fetching the paper…
Reading the bibliography…
Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored.
Identifiability and exchangeability for direct and indirect effects
James M Robins and Sander Greenland · 1992
Earlier work this paper cites.
Direct and indirect effects
Judea Pearl · 2001
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
Wes McKinney · 2010
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Collaborative data science, 2015
Plotly Technologies Inc · 2015
Earlier work this paper cites.
Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2015
Earlier work this paper cites.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 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
Earlier work this paper cites.
Fixing weight decay regularization in adam, 2018
Ilya Loshchilov and Frank Hutter · 2018
Earlier work this paper cites.
Learning to generate reviews and discovering sentiment, 2018
Alec Radford, Rafal Jozefowicz, and Ilya Sutskever · 2018
Earlier work this paper cites.
What is one grain of sand in the desert? analyzing individual neurons in deep nlp models
Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, and James Glass · 2019
Earlier work this paper cites.
Uncertainty in deep learning, 2016
Yarin Gal et al · 2019
Earlier work this paper cites.
Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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
Earlier work this paper cites.
Analyzing the structure of attention in a transformer language model
Jesse Vig and Yonatan Belinkov · 2019
Earlier work this paper cites.
Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Earlier work this paper cites.
Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
Earlier work this paper cites.
Interpreting gpt: The logit lens, 2020
Nostalgebraist · 2020
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
Earlier work this paper cites.
Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy · 2021
Earlier work this paper cites.
Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2021
Cited alongside, same era.
How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
Cited alongside, same era.
Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark Gales · 2021
Cited alongside, same era.
Transformer visualization via dictionary learning: Contextualized embedding as a linear superposition of transformer factors
Zeyu Yun, Yubei Chen, Bruno Olshausen, and Yann LeCun · 2021
Cited alongside, same era.
On the pitfalls of analyzing individual neurons in language models
Omer Antverg and Yonatan Belinkov · 2022
Cited alongside, same era.
Decomposing uncertainty for large language models through input clarification ensembling
Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, and Yang Zhang · 2023
Later among the works it cites.
Does it know?: Probing for uncertainty in language model latent beliefs
Brian RY Huang and Joe Kwon · 2023
Later among the works it cites.
Phi-2: The surprising power of small language models
Mojan Javaheripi, Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, et al · 2023
Later among the works it cites.
VISIT: Visualizing and interpreting the semantic information flow of transformers
Shahar Katz and Yonatan Belinkov · 2023
Later among the works it cites.
Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Word acquisition in neural language models
Tyler A. Chang and Benjamin K. Bergen · 2022
Cited alongside, same era.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 2022
Cited alongside, same era.
Softmax linear units
Nelson Elhage, Tristan Hume, Catherine Olsson, Neel Nanda, Tom Henighan, Scott Johnston, Sheer ElShowk, Nicholas Joseph, Nova DasSarma, Ben Mann, Danny Hernandez, Amanda Askell, Kamal Ndousse, Andy Jones, Dawn Drain, Anna Chen, Yuntao Bai, Deep Ganguli, Liane Lovitt, Zac Hatfield-Dodds, Jackson Kernion, Tom Conerly, Shauna Kravec, Stanislav Fort, Saurav Kadavath, Josh Jacobson, Eli Tran-Johnson, Jared Kaplan, Jack Clark, Tom Brown, Sam McCandlish, Dario Amodei, and Christopher Olah · 2022
Cited alongside, same era.
Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Cited alongside, same era.
Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex J Andonian, and Yonatan Belinkov · 2022
Cited alongside, same era.
Tom Lieberum, Matthew Rahtz, János Kramár, Geoffrey Irving, Rohin Shah, and Vladimir Mikulik · 2023
Later among the works it cites.
A natural bias for language generation models
Clara Meister, Wojciech Stokowiec, Tiago Pimentel, Lei Yu, Laura Rimell, and Adhiguna Kuncoro · 2023
Later among the works it cites.
Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
Later among the works it cites.
Prompting GPT-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Lee Boyd-Graber, and Lijuan Wang · 2023
Later among the works it cites.
A mechanistic interpretation of arithmetic reasoning in language models using causal mediation analysis
Alessandro Stolfo, Yonatan Belinkov, and Mrinmaya Sachan · 2023
Later among the works it cites.
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher Manning · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Neurons in large language models: Dead, n-gram, positional
Elena Voita, Javier Ferrando, and Christoforos Nalmpantis · 2023
Later among the works it cites.
Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2023
Later among the works it cites.
Ryanize bib, 2023
Vilém Zouhar · 2023
Later among the works it cites.
Managing extreme AI risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, and Sören Mindermann · 2024
Closest in time.
Spectral filters, dark signals, and attention sinks
Nicola Cancedda · 2024
Closest in time.
A primer on the inner workings of transformer-based language models
Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R Costa-jussà · 2024
Closest in time.
Gemini: A family of highly capable multimodal models, 2024
Gemini Team Google · 2024
Closest in time.
Universal neurons in GPT2 language models
Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, and Dimitris Bertsimas · 2024
Closest in time.
Sparse autoencoders find highly interpretable features in language models
Robert Huben, Hoagy Cunningham, Logan Riggs Smith, Aidan Ewart, and Lee Sharkey · 2024
Closest in time.
Sparse autoencoders work on attention layer outputs
Connor Kissane, Robert Krzyzanowski, Arthur Conmy, and Neel Nanda · 2024
Closest in time.
We inspected every head in GPT-2 small using SAEs so you don’t have to
Robert Krzyzanowski, Connor Kissane, Arthur Conmy, and Neel Nanda · 2024
Closest in time.
The geometry of truth: Emergent linear structure in large language model representations of true/false datasets, 2024
Samuel Marks and Max Tegmark · 2024
Closest in time.
OpenAI · 2024
Closest in time.
Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
Closest in time.