Fetching the paper…
Reading the bibliography…
In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity.
The bargaining problem
John F Nash et al · 1950
Earlier work this paper cites.
Manipulation of voting schemes: a general result
Allan Gibbard · 1973
Earlier work this paper cites.
Equity, envy, and efficiency
Hal R Varian · 1973
Earlier work this paper cites.
Strategy-proofness and arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions
Mark Allen Satterthwaite · 1975
Earlier work this paper cites.
Handbook of mathematical economics , volume 1
Kenneth Joseph Arrow, Michael D Intriligator, Werner Hildenbrand, and Hugo Sonnenschein · 1981
Earlier work this paper cites.
Optimal auction design
Roger B Myerson · 1981
Earlier work this paper cites.
Charging and rate control for elastic traffic
Frank Kelly · 1997
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Randomized truthful auctions of digital goods are randomizations over truthful auctions
Aranyak Mehta and Vijay V Vazirani · 2004
Earlier work this paper cites.
Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords
Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz · 2007
Earlier work this paper cites.
Position auctions
Hal R Varian · 2007
Earlier work this paper cites.
Vcg-kelly mechanisms for allocation of divisible goods: Adapting vcg mechanisms to one-dimensional signals
Sichao Yang and Bruce Hajek · 2007
Earlier work this paper cites.
Discrete choice methods with simulation
Kenneth E Train · 2009
Earlier work this paper cites.
Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine
Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich · 2010
Earlier work this paper cites.
An axiomatic theory of fairness in network resource allocation
Tian Lan, David Kao, Mung Chiang, and Ashutosh Sabharwal · 2010
Earlier work this paper cites.
Algorithmic game theory
Tim Roughgarden · 2010
Earlier work this paper cites.
The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes
Eric Budish · 2011
Cited alongside, same era.
Ad click prediction: a view from the trenches
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
Cited alongside, same era.
Arrow impossibility theorems
Jerry S Kelly · 2014
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Cited alongside, same era.
The unreasonable fairness of maximum nash welfare
Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D Procaccia, Nisarg Shah, and Junxing Wang · 2019
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Later among the works it cites.
Retrieval-based language models and applications
Akari Asai, Sewon Min, Zexuan Zhong, and Danqi Chen · 2023
Later among the works it cites.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston · 2023
Later among the works it cites.
Mechanism design for large language models
Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, and Song Zuo · 2023
Later among the works it cites.
Online advertisements with llms: Opportunities and challenges
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Gpt-j-6b: A 6 billion parameter autoregressive language model, 2021
Ben Wang and Aran Komatsuzaki · 2021
Cited alongside, same era.
Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang · 2022
Cited alongside, same era.
Soheil Feizi, MohammadTaghi Hajiaghayi, Keivan Rezaei, and Suho Shin · 2023
Later among the works it cites.
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
Later among the works it cites.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 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
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
Later among the works it cites.
Improving text embeddings with large language models
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei · 2023
Later among the works it cites.
Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, and Di Wang · 2024
Closest in time.
Large language models: A survey
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao · 2024
Closest in time.
ChatGPT Pricing
OpenAI · 2024
Closest in time.
Truthful aggregation of llms with an application to online advertising
Ermis Soumalias, Michael J Curry, and Sven Seuken · 2024
Closest in time.
Mechanism design for llm fine-tuning with multiple reward models
Haoran Sun, Yurong Chen, Siwei Wang, Wei Chen, and Xiaotie Deng · 2024
Closest in time.