Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have demonstrated remarkable abilities in text comprehension and logical reasoning, indicating that the text representations learned by LLMs can facilitate their language processing capabilities.
Correlating neural and symbolic representations of language
Grzegorz Chrupała and Afra Alishahi. 2019 · 1905
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 1905
Earlier work this paper cites.
Samira Abnar, Lisa Beinborn, Rochelle Choenni, and Willem Zuidema. 2019 · 1906
Earlier work this paper cites.
Higher-order comparisons of sentence encoder representations
Mostafa Abdou, Artur Kulmizev, Felix Hill, Daniel M Low, and Anders Søgaard. 2019 · 1909
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.
Affective neuroscience and psychophysiology: Toward a synthesis
Richard J Davidson. 2003 · 2003
Earlier work this paper cites.
Representational similarity analysis-connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini. 2008 · 2008
Earlier work this paper cites.
Predicting human brain activity associated with the meanings of nouns
Tom M Mitchell, Svetlana V Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L Malave, Robert A Mason, and Marcel Adam Just. 2008 · 2008
Earlier work this paper cites.
Natural Language Processing with Python
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
Earlier work this paper cites.
The brain basis of language processing: from structure to function
Angela D Friederici. 2011 · 2011
Earlier work this paper cites.
The representation of biological classes in the human brain
Andrew C Connolly, J Swaroop Guntupalli, Jason Gors, Michael Hanke, Yaroslav O Halchenko, Yu-Chien Wu, Hervé Abdi, and James V Haxby. 2012 · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Aligning context-based statistical models of language with brain activity during reading
Leila Wehbe, Ashish Vaswani, Kevin Knight, and Tom Mitchell. 2014 · 2014
Earlier work this paper cites.
The brain as an efficient and robust adaptive learner
Sophie Denève, Alireza Alemi, and Ralph Bourdoukan. 2017 · 2017
Earlier work this paper cites.
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis
Jörn Diedrichsen and Nikolaus Kriegeskorte. 2017 · 2017
Earlier work this paper cites.
Toward a universal decoder of linguistic meaning from brain activation
Francisco Pereira, Bin Lou, Brianna Pritchett, Samuel Ritter, Samuel J Gershman, Nancy Kanwisher, Matthew Botvinick, and Evelina Fedorenko. 2018 · 2018
Earlier work this paper cites.
Representation similarity analysis for efficient task taxonomy & transfer learning
Kshitij Dwivedi and Gemma Roig. 2019 · 2019
Cited alongside, same era.
Cognival: A framework for cognitive word embedding evaluation
Nora Hollenstein, Antonio de la Torre, Nicolas Langer, and Ce Zhang. 2019 · 2019
Cited alongside, same era.
What does bert learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Cited alongside, same era.
Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images
Marcie L. King, Iris I. A. Groen, Adam Steel, Dwight J. Kravitz, and Chris I. Baker. 2019 · 2019
Cited alongside, same era.
Towards sentence-level brain decoding with distributed representations
Jingyuan Sun, Shaonan Wang, Jiajun Zhang, and Chengqing Zong. 2019 · 2019
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, et al. 2022 · 2022
Later among the works it cites.
Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. 2023 · 2023
Later among the works it cites.
Dis/similarities in the design and development of legal and algorithmic normative systems: the case of perspective api
Paul Friedl. 2023 · 2023
Later among the works it cites.
The capacity for moral self-correction in large language models
Deep Ganguli, Amanda Askell, Nicholas Schiefer, Thomas I Liao, Kamilė Lukošiūtė, Anna Chen, Anna Goldie, Azalia Mirhoseini, Catherine Olsson, Danny Hernandez, et al. 2023 · 2023
Later among the works it cites.
In-context alignment: Chat with vanilla language models before fine-tuning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Mariya Toneva and Leila Wehbe. 2019 · 2019
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Cogalign: Learning to align textual neural representations to cognitive language processing signals
Yuqi Ren and Deyi Xiong. 2021 · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Brainbench: A brain-image test suite for distributional semantic models
Haoyan Xu, Brian Murphy, and Alona Fyshe. 2016 · 2021
Cited alongside, same era.
Analyzing and addressing the difference in toxicity prediction between different comments with same semantic meaning in google’s perspective api
SK Gargee, Pranav Bhargav Gopinath, Shridhar Reddy SR Kancharla, CR Anand, and Anoop S Babu. 2022 · 2022
Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022 · 2022
Cited alongside, same era.
Xiaochuang Han. 2023 · 2023
Later among the works it cites.
Emotionally numb or empathetic? evaluating how LLMs feel using emotionbench
Jen-tse Huang, Man Ho Lam, Eric John Li, Shujie Ren, Wenxuan Wang, Wenxiang Jiao, Zhaopeng Tu, and Michael R Lyu. 2023 · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Later among the works it cites.
Alpacaeval: An automatic evaluator of instruction-following models
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
Later among the works it cites.
LLM360: Towards fully transparent open-source llms
Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, and Eric P. Xing. 2023 · 2023
Later among the works it cites.
Introducing chatgpt. web link
OpenAI. 2023 · 2023
Later among the works it cites.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 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 · 2023
Later among the works it cites.
Zephyr: Direct distillation of LM alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023 · 2023
Later among the works it cites.
Emotional intelligence of large language models
Xuena Wang, Xueting Li, Zi Yin, Yue Wu, and Jia Liu. 2023 · 2023
Later among the works it cites.
Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023 · 2023
Later among the works it cites.
Introducing meta LLaMA 3: The most capable openly available llm to date. web link
Meta. 2024 · 2024
Closest in time.