Fetching the paper…
Reading the bibliography…
Understanding the quality of a performance evaluation metric is crucial for ensuring that model outputs align with human preferences.
Perplexity—a measure of the difficulty of speech recognition tasks
Fred Jelinek, Robert L Mercer, Lalit R Bahl, and James K Baker · 1977
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
Earlier work this paper cites.
Re-evaluating the role of bleu in machine translation research
Chris Callison-Burch, Miles Osborne, and Philipp Koehn · 2006
Earlier work this paper cites.
Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
Earlier work this paper cites.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
Earlier work this paper cites.
Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Earlier work this paper cites.
Better summarization evaluation with word embeddings for rouge
Jun-Ping Ng and Viktoria Abrecht · 2015
Earlier work this paper cites.
chrf: character n-gram f-score for automatic mt evaluation
Maja Popović · 2015
Earlier work this paper cites.
Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
Earlier work this paper cites.
Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al · 2016
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
High dimensional bayesian optimization via restricted projection pursuit models
Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, and Jeff Schneider · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
P Rajpurkar · 2016
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
Earlier work this paper cites.
A tutorial on bayesian optimization
Peter I Frazier · 2018
Earlier work this paper cites.
The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
Semeval-2018 task 11: Machine comprehension using commonsense knowledge
Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, and Manfred Pinkal · 2018
Earlier work this paper cites.
A call for clarity in reporting bleu scores
Matt Post · 2018
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Earlier work this paper cites.
Evaluating question answering evaluation
Anthony Chen, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
Cited alongside, same era.
Mrqa 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen · 2019
Cited alongside, same era.
Tiger: Text-to-image grounding for image caption evaluation
Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, and Jianfeng Gao · 2019
Cited alongside, same era.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
Cited alongside, same era.
A framework for bayesian optimization in embedded subspaces
Amin Nayebi, Alexander Munteanu, and Matthias Poloczek · 2019
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Comet-22: Unbabel-ist 2022 submission for the metrics shared task
Ricardo Rei, José GC De Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André FT Martins · 2022
Later among the works it cites.
Towards a unified multi-dimensional evaluator for text generation
Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han · 2022
Later among the works it cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Later among the works it cites.
Stable video diffusion: Scaling latent video diffusion models to large datasets
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller · 2019
Cited alongside, same era.
Answers unite! unsupervised metrics for reinforced summarization models
Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano · 2019
Cited alongside, same era.
Learning multilingual meta-embeddings for code-switching named entity recognition
Genta Indra Winata, Zhaojiang Lin, and Pascale Fung · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
Cited alongside, same era.
Moverscore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M Meyer, and Steffen Eger · 2019
Cited alongside, same era.
Towards a human-like open-domain chatbot
Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al · 2023
Later among the works it cites.
Results of wmt23 metrics shared task: Metrics might be guilty but references are not innocent
Markus Freitag, Nitika Mathur, Chi-kiu Lo, Eleftherios Avramidis, Ricardo Rei, Brian Thompson, Tom Kocmi, Frédéric Blain, Daniel Deutsch, Craig Stewart, et al · 2023
Later among the works it cites.
Rogue scores
Max Grusky · 2023
Later among the works it cites.
xcomet: Transparent machine translation evaluation through fine-grained error detection
Nuno M Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, and André FT Martins · 2023
Later among the works it cites.
Metricx-23: The google submission to the wmt 2023 metrics shared task
Juraj Juraska, Mara Finkelstein, Daniel Deutsch, Aditya Siddhant, Mehdi Mirzazadeh, and Markus Freitag · 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.
G-eval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu · 2023
Later among the works it cites.
The decades progress on code-switching research in nlp: A systematic survey on trends and challenges
Genta Winata, Alham Fikri Aji, Zheng Xin Yong, and Thamar Solorio · 2023
Later among the works it cites.
Towards measuring and modeling” culture” in llms: A survey
Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O’Neill, Ashutosh Modi, and Monojit Choudhury · 2024
Closest in time.
Proxylm: Predicting language model performance on multilingual tasks via proxy models
David Anugraha, Genta Indra Winata, Chenyue Li, Patrick Amadeus Irawan, and En-Shiun Annie Lee · 2024
Closest in time.
Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, et al · 2024
Closest in time.
Are llms breaking mt metrics? results of the wmt24 metrics shared task
Markus Freitag, Nitika Mathur, Daniel Deutsch, Chi-Kiu Lo, Eleftherios Avramidis, Ricardo Rei, Brian Thompson, Frédéric Blain, Tom Kocmi, Jiayi Wang, et al · 2024
Closest in time.
Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms
Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri · 2024
Closest in time.
Metricx-24: The google submission to the wmt 2024 metrics shared task
Juraj Juraska, Daniel Deutsch, Mara Finkelstein, and Markus Freitag · 2024
Closest in time.
Garry Kuwanto, Chaitanya Agarwal, Genta Indra Winata, and Derry Tanti Wijaya · 2024
Closest in time.
Rewardbench: Evaluating reward models for language modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, et al · 2024
Closest in time.
Offsetbias: Leveraging debiased data for tuning evaluators
Junsoo Park, Seungyeon Jwa, Meiying Ren, Daeyoung Kim, and Sanghyuk Choi · 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.
Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, and Bill Yuchen Lin · 2024
Closest in time.
Pangea: A fully open multilingual multimodal llm for 39 languages
Xiang Yue, Yueqi Song, Akari Asai, Seungone Kim, Jean de Dieu Nyandwi, Simran Khanuja, Anjali Kantharuban, Lintang Sutawika, Sathyanarayanan Ramamoorthy, and Graham Neubig · 2024
Closest in time.
Benchmarking large language models for news summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto · 2024
Closest in time.
Bat: Learning to reason about spatial sounds with large language models
Zhisheng Zheng, Puyuan Peng, Ziyang Ma, Xie Chen, Eunsol Choi, and David Harwath · 2024
Closest in time.