Fetching the paper…
Reading the bibliography…
To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers.
Selected studies of the principle of relative frequency in language
George Kingsley Zipf · 1932
Earlier work this paper cites.
Error detecting and error correcting codes
Richard W Hamming · 1950
Earlier work this paper cites.
Some elementary inequalities relating to the gamma and incomplete gamma function
Walter Gautschi · 1959
Earlier work this paper cites.
Polynomial codes over certain finite fields
Irving S Reed and Gustave Solomon · 1960
Earlier work this paper cites.
Low-density parity-check codes
Robert Gallager · 1962
Earlier work this paper cites.
Information theory and psycholinguistics
Benoît Mandelbrot · 1965
Earlier work this paper cites.
Finite exchangeable sequences
Persi Diaconis and David Freedman · 1980
Earlier work this paper cites.
On a source-coding problem with two channels and three receivers
L. Ozarow · 1980
Earlier work this paper cites.
Achievable rates for multiple descriptions
A.E. Gamal and T. Cover · 1982
Earlier work this paper cites.
Automatic-repeat-request error-control schemes
Shu Lin, Daniel J Costello, and Michael J Miller · 1984
Earlier work this paper cites.
Distance based ranking models
M. A. Fligner and J. S. Verducci · 1986
Earlier work this paper cites.
Near shannon limit error-correcting coding and decoding: Turbo-codes. 1
Claude Berrou, Alain Glavieux, and Punya Thitimajshima · 1993
Earlier work this paper cites.
Applications and explanations of zipf’s law
David MW Powers · 1998
Earlier work this paper cites.
Minimum Bayes-risk word alignments of bilingual texts
Shankar Kumar and William Byrne · 2002
Earlier work this paper cites.
Information Theory, Inference & Learning Algorithms
David J. C. MacKay · 2002
Earlier work this paper cites.
Order Statistics
H.A. David and H.N. Nagaraja · 2004
Earlier work this paper cites.
Source-channel diversity for parallel channels
J.N. Laneman, E. Martinian, G.W. Wornell, and J.G. Apostolopoulos · 2005
Earlier work this paper cites.
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Thomas M. Cover and Joy A. Thomas · 2006
Earlier work this paper cites.
Unsupervised rank aggregation with distance-based models
Alexandre Klementiev, Dan Roth, and Kevin Small · 2008
Earlier work this paper cites.
Non-parametric modeling of partially ranked data
Guy Lebanon and Yi Mao · 2008
Earlier work this paper cites.
Unsupervised rank aggregation with domain-specific expertise
Alexandre Klementiev, Dan Roth, Kevin Small, and Ivan Titov · 2009
Earlier work this paper cites.
Deep learning scaling is predictable, empirically, 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
Earlier work this paper cites.
Mallows models for top-k lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, and Silvio Lattanzi · 2018
Earlier work this paper cites.
Adaptively sparse transformers
Gonçalo M. Correia, Vlad Niculae, and André F. T. Martins · 2019
Earlier work this paper cites.
Beyond human-level accuracy: computational challenges in deep learning
Joel Hestness, Newsha Ardalani, and Gregory Diamos · 2019
Earlier work this paper cites.
Sparse sequence-to-sequence models
Ben Peters, Vlad Niculae, and André F. T. Martins · 2019
Earlier work this paper cites.
Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
Earlier work this paper cites.
Mallows ranking models: maximum likelihood estimate and regeneration
Wenpin Tang · 2019
Earlier work this paper cites.
TICO-19: the translation initiative for COvid-19
Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou, Marcello Federico, Christian Federmann, Dmitriy Genzel, Franscisco Guzmán, Junjie Hu, Macduff Hughes, Philipp Koehn, Rosie Lazar, Will Lewis, Graham Neubig, Mengmeng Niu, Alp Öktem, Eric Paquin, Grace Tang, and Sylwia Tur · 2020
Cited alongside, same era.
Learning with fenchel-young losses
Mathieu Blondel, André F.T. Martins, and Vlad Niculae · 2020
Cited alongside, same era.
Leveraging sentence similarity in natural language generation: Improving beam search using range voting
Sebastian Borgeaud and Guy Emerson · 2020
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Efficient marginalization of discrete and structured latent variables via sparsity
Gonçalo Correia, Vlad Niculae, Wilker Aziz, and André Martins · 2020
It’s MBR all the way down: Modern generation techniques through the lens of minimum Bayes risk
Amanda Bertsch, Alex Xie, Graham Neubig, and Matthew Gormley · 2023
Later among the works it cites.
An empirical study of translation hypothesis ensembling with large language models
António Farinhas, José de Souza, and Andre Martins · 2023
Later among the works it cites.
The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation
Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, Parker Riley, André Martins, Graham Neubig, Ankush Garg, Jonathan Clark, Markus Freitag, and Orhan Firat · 2023
Later among the works it cites.
Hallucinations in Large Multilingual Translation Models
Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, and André F. T. Martins · 2023
Later among the works it cites.
Walking a tightrope – evaluating large language models in high-risk domains
Chia-Chien Hung, Wiem Ben Rim, Lindsay Frost, Lars Bruckner, and Carolin Lawrence · 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…
Cited alongside, same era.
Is MAP decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz · 2020
Cited alongside, same era.
Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
Sparse and continuous attention mechanisms
André Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro Aguiar, and Mario Figueiredo · 2020
Cited alongside, same era.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
Cited alongside, same era.
Program synthesis with large language models, 2021
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton · 2021
Cited alongside, same era.
Energy-based reranking: Improving neural machine translation using energy-based models
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum · 2021
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Later among the works it cites.
GEMBA-MQM: Detecting translation quality error spans with GPT-4
Tom Kocmi and Christian Federmann · 2023
Later among the works it cites.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and LINGMING ZHANG · 2023
Later among the works it cites.
LEVER: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida Wang, and Xi Victoria Lin · 2023
Later among the works it cites.
Gpt-4 technical report, 2023
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Jan Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O’Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang Song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, and Barret Zoph · 2023
Later among the works it cites.
Conformal language modeling
Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S Jaakkola, and Regina Barzilay · 2023
Later among the works it cites.
Eel: Efficiently encoding lattices for reranking
Prasann Singhal, Jiacheng Xu, Xi Ye, and Greg Durrett · 2023
Later among the works it cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
Later among the works it cites.
Tower: An open multilingual large language model for translation-related tasks, 2024
Duarte M. Alves, José Pombal, Nuno M. Guerreiro, Pedro H. Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, José G. C. de Souza, and André F. T. Martins · 2024
Closest in time.
Llms instead of human judges? a large scale empirical study across 20 nlp evaluation tasks, 2024
Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, André F. T. Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, and Alberto Testoni · 2024
Closest in time.
Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang · 2024
Closest in time.
Deal: Decoding-time alignment for large language models
James Y Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchoff, and Dan Roth · 2024
Closest in time.
Impossible distillation: from low-quality model to high-quality dataset & model for summarization and paraphrasing
Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, and Yejin Choi · 2024
Closest in time.
Position: LLMs can’t plan, but can help planning in LLM-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Paul Saldyt, and Anil B Murthy · 2024
Closest in time.
Prometheus: Inducing fine-grained evaluation capability in language models
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo · 2024
Closest in time.
Doce: Finding the sweet spot for execution-based code generation, 2024
Haau-Sing Li, Patrick Fernandes, Iryna Gurevych, and André F. T. Martins · 2024
Closest in time.
Can large language models reason about medical questions?
Valentin Liévin, Christoffer Egeberg Hother, Andreas Geert Motzfeldt, and Ole Winther · 2024
Closest in time.
Decoding-time realignment of language models
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, and Mathieu Blondel · 2024
Closest in time.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
Closest in time.
Don’t rank, combine! combining machine translation hypotheses using quality estimation
Giorgos Vernikos and Andrei Popescu-Belis · 2024
Closest in time.
Executable code actions elicit better LLM agents
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji · 2024
Closest in time.
Rrhf: Rank responses to align language models with human feedback
Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2024
Closest in time.