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Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation.
Language models are few-shot learners
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Huggingface’s transformers: State-of-the-art natural language processing
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Bleu: a method for automatic evaluation of machine translation
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Longformer: The long-document transformer
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ROUGE: A package for automatic evaluation of summaries
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Adversarial nli for factual correctness in text summarisation models
Mario Barrantes, Benedikt Herudek, and Richard Wang. 2020 · 2005
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020b · 2005
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Evaluation of text generation: A survey
Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020 · 2006
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Non-expert evaluation of summarization systems is risky
Dan Gillick and Yang Liu. 2010 · 2010
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Scikit-learn: Machine learning in Python
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Texygen: A benchmarking platform for text generation models
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ELI5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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The curious case of neural text degeneration
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How to compare summarizers without target length? pitfalls, solutions and re-examination of the neural summarization literature
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Bertscore: Evaluating text generation with bert
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MOCHA: A dataset for training and evaluating generative reading comprehension metrics
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ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
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Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
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Best practices for crowd-based evaluation of German summarization: Comparing crowd, expert and automatic evaluation
Neslihan Iskender, Tim Polzehl, and Sebastian Möller. 2020 · 2020
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Bartscore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
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QAFactEval: Improved QA-based factual consistency evaluation for summarization
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022 · 2022
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Attributed text generation via post-hoc research and revision
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Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
Sebastian Gehrmann, Elizabeth Clark, and Thibault Sellam. 2022 · 2022
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Snac - coherence error detection for narrative summarization
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Stanza: A python natural language processing toolkit for many human languages
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2020
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Language generation via combinatorial constraint satisfaction: A tree search enhanced Monte-Carlo approach
Maosen Zhang, Nan Jiang, Lei Li, and Yexiang Xue. 2020 · 2020
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Bidimensional leaderboards: Generate and evaluate language hand in hand
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander Fabbri, Yejin Choi, and Noah A. Smith. 2022 · 2022
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Rankgen: Improving text generation with large ranking models
Kalpesh Krishna, Yapei Chang, John Wieting, and Mohit Iyyer. 2022 · 2022
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Summac: Re-visiting nli-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022 · 2022
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Evaluating human-language model interaction
Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, and Percy Liang. 2022 · 2022
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Revisiting the gold standard: Grounding summarization evaluation with robust human evaluation
Yixin Liu, Alexander R. Fabbri, Pengfei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq R. Joty, Chien-Sheng Wu, Caiming Xiong, and Dragomir R. Radev. 2022 · 2022
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Cold decoding: Energy-based constrained text generation with langevin dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi. 2022 · 2022
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Improving passage retrieval with zero-shot question generation
Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022 · 2022
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Asqa: Factoid questions meet long-form answers
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2022 · 2022
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Read before generate! faithful long form question answering with machine reading
Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 2022 · 2022
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Modeling exemplification in long-form question answering via retrieval
Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, and Mohit Iyyer. 2022 · 2022
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Fined-eval: Fine-grained automatic dialogue-level evaluation
Chen Zhang, L. F. D’Haro, Qiquan Zhang, Thomas Friedrichs, and Haizhou Li. 2022 · 2022
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Towards a unified multi-dimensional evaluator for text generation
Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Peng Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022 · 2022
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