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Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks.
Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
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Complete functional characterization of sensory neurons by system identification
Michael C.-K. Wu, Stephen V. David, and Jack L. Gallant · 2006
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Ga ë l Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Aligning context-based statistical models of language with brain activity during reading
Leila Wehbe, Ashish Vaswani, Kevin Knight, and Tom Mitchell · 2014
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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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European union regulations on algorithmic decision-making and a" right to explanation"
Bryce Goodman and Seth Flaxman · 2016
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Natural speech reveals the semantic maps that tile human cerebral cortex
Alexander G Huth, Wendy A De Heer, Thomas L Griffiths, Frédéric E Theunissen, and Jack L Gallant · 2016
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The occipital place area is causally involved in representing environmental boundaries during navigation
Joshua B Julian, Jack Ryan, Roy H Hamilton, and Russell A Epstein · 2016
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Ms marco: A human-generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng · 2016
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A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni · 2018
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Incorporating context into language encoding models for fmri
Shailee Jain and Alexander Huth · 2018
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Retrosplenial cortex and its role in spatial cognition
Anna S Mitchell, Rafal Czajkowski, Ningyu Zhang, Kate Jeffery, and Andrew JD Nelson · 2018
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen · 2018
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Incorporating priors with feature attribution on text classification
Frederick Liu and Besim Avci · 2019
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Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Mariya Toneva and Leila Wehbe · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Colbert: Efficient and effective passage search via contextualized late interaction over bert
Omar Khattab and Matei Zaharia · 2020
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Learning conceptual-contextual embeddings for medical text
Xiao Zhang, Dejing Dou, and Ji Wu · 2020
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Artificial intelligence, values, and alignment
Iason Gabriel · 2020
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Understanding the role of individual units in a deep neural network
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba · 2020
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Interpretable multi-timescale models for predicting fmri responses to continuous natural speech
Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S Turek, and Alexander Huth · 2020
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Can fMRI reveal the representation of syntactic structure in the brain?
Aniketh Janardhan Reddy and Leila Wehbe · 2020
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Ultra-high dimensional sparse representations with binarization for efficient text retrieval
Kyoung-Rok Jang, Junmo Kang, Giwon Hong, Sung-Hyon Myaeng, Joohee Park, Taewon Yoon, and Heecheol Seo · 2021
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Toward a visual concept vocabulary for gan latent space
Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, and Antonio Torralba · 2021
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The neural architecture of language: Integrative modeling converges on predictive processing
Martin Schrimpf, Idan Asher Blank, Greta Tuckute, Carina Kauf, Eghbal A Hosseini, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko · 2021
Finding neurons in a haystack: Case studies with sparse probing, 2023
Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, and Dimitris Bertsimas · 2023
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Explaining language models’ predictions with high-impact concepts
Ruochen Zhao, Shafiq Joty, Yongjie Wang, and Tan Wang · 2023
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
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Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar · 2023
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Concept bottleneck generative models
Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, and Kyunghyun Cho · 2023
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Cited alongside, same era.
Visual and linguistic semantic representations are aligned at the border of human visual cortex
Sara F Popham, Alexander G Huth, Natalia Y Bilenko, Fatma Deniz, James S Gao, Anwar O Nunez-Elizalde, and Jack L Gallant · 2021
Cited alongside, same era.
Disentangling syntax and semantics in the brain with deep networks
Charlotte Caucheteux, Alexandre Gramfort, and Jean-Remi King · 2021
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The lateral intraparietal sulcus takes viewpoint changes into account during memory-guided attention in natural scenes
Ilenia Salsano, Valerio Santangelo, and Emiliano Macaluso · 2021
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein · 2021
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Sgpt: Gpt sentence embeddings for semantic search
Niklas Muennighoff · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2022
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Explaining patterns in data with language models via interpretable autoprompting
Chandan Singh, John X Morris, Jyoti Aneja, Alexander M Rush, and Jianfeng Gao · 2022
Cited alongside, same era.
Sim2word: Explaining similarity with representative attribute words via counterfactual explanations
Ruoyu Chen, Jingzhi Li, Hua Zhang, Changchong Sheng, Li Liu, and Xiaochun Cao · 2023
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Computational Language Modeling and the Promise of in Silico Experimentation
Shailee Jain, Vy A. Vo, Leila Wehbe, and Alexander G. Huth · 2023
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The cortical representation of language timescales is shared between reading and listening
Catherine Chen, Tom Dupré la Tour, Jack Gallant, Daniel Klein, and Fatma Deniz · 2023
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Lexical semantic content, not syntactic structure, is the main contributor to ann-brain similarity of fmri responses in the language network
Carina Kauf, Greta Tuckute, Roger Levy, Jacob Andreas, and Evelina Fedorenko · 2023
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Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, and Christophe Pallier · 2023
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Semantic reconstruction of continuous language from non-invasive brain recordings
Jerry Tang, Amanda LeBel, Shailee Jain, and Alexander G Huth · 2023
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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
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Language models and cognitive automation for economic research
Anton Korinek · 2023
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Goal driven discovery of distributional differences via language descriptions
Ruiqi Zhong, Peter Zhang, Steve Li, Jinwoo Ahn, Dan Klein, and Jacob Steinhardt · 2023
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen · 2023
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Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P Xing, and Zhiting Hu · 2023
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Computational language modeling and the promise of in silico experimentation
Shailee Jain, Vy A Vo, Leila Wehbe, and Alexander G Huth · 2024
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Rethinking interpretability in the era of large language models
Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, and Jianfeng Gao · 2024
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Llama 3 model card
AI@Meta · 2024
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Repetition improves language model embeddings
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig, and Aditi Raghunathan · 2024
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Gecko: Versatile text embeddings distilled from large language models, 2024
Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, Michael Boratko, Rajvi Kapadia, Wen Ding, Yi Luan, Sai Meher Karthik Duddu, Gustavo Hernandez Abrego, Weiqiang Shi, Nithi Gupta, Aditya Kusupati, Prateek Jain, Siddhartha Reddy Jonnalagadda, Ming-Wei Chang, and Iftekhar Naim · 2024
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Promptreps: Prompting large language models to generate dense and sparse representations for zero-shot document retrieval, 2024
Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon · 2024
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Answer is all you need: Instruction-following text embedding via answering the question
Letian Peng, Yuwei Zhang, Zilong Wang, Jayanth Srinivasa, Gaowen Liu, Zihan Wang, and Jingbo Shang · 2024
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Describe-and-dissect: Interpreting neurons in vision networks with language models
Nicholas Bai, Rahul A Iyer, Tuomas Oikarinen, and Tsui-Wei Weng · 2024
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Uncovering meanings of embeddings via partial orthogonality
Yibo Jiang, Bryon Aragam, and Victor Veitch · 2024
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Interpreting clip with sparse linear concept embeddings (splice)
Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flavio P Calmon, and Himabindu Lakkaraju · 2024
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Scaling laws for language encoding models in fmri
Richard Antonello, Aditya Vaidya, and Alexander Huth · 2024
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The language network as a natural kind within the broader landscape of the human brain
Evelina Fedorenko, Anna A Ivanova, and Tamar I Regev · 2024
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Raptor: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning · 2024
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From local to global: A graph rag approach to query-focused summarization
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson · 2024
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Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston · 2024
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Deductive closure training of language models for coherence, accuracy, and updatability
Afra Feyza Akyürek, Ekin Akyürek, Leshem Choshen, Derry Wijaya, and Jacob Andreas · 2024
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Towards consistent natural-language explanations via explanation-consistency finetuning
Yanda Chen, Chandan Singh, Xiaodong Liu, Simiao Zuo, Bin Yu, He He, and Jianfeng Gao · 2024
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