Fetching the paper…
Reading the bibliography…
Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
Using latent semantic analysis to improve access to textual information
Susan T Dumais, George W Furnas, Thomas K Landauer, Scott Deerwester, and Richard Harshman. 1988 · 1988
Earlier work this paper cites.
The well-built clinical question: a key to evidence-based decisions
W Scott Richardson, Mark C Wilson, Jim Nishikawa, and Robert S Hayward. 1995 · 1995
Earlier work this paper cites.
How quickly do systematic reviews go out of date? a survival analysis
Kaveh G Shojania, Margaret Sampson, Mohammed T Ansari, Jun Ji, Steve Doucette, and David Moher. 2007 · 2007
Earlier work this paper cites.
Reading tea leaves: How humans interpret topic models
Jonathan Chang, Sean Gerrish, Chong Wang, Jordan Boyd-Graber, and David Blei. 2009 · 2009
Earlier work this paper cites.
Using citations to generate surveys of scientific paradigms
Saif Mohammad, Bonnie Dorr, Melissa Egan, Ahmed Hassan, Pradeep Muthukrishan, Vahed Qazvinian, Dragomir Radev, and David Zajic. 2009 · 2009
Earlier work this paper cites.
Towards automated related work summarization
Cong Duy Vu Hoang and Min-Yen Kan. 2010 · 2010
Earlier work this paper cites.
Automatic generation of related work sections in scientific papers: An optimization approach
Yue Hu and Xiaojun Wan. 2014 · 2014
Earlier work this paper cites.
Content models for survey generation: A factoid-based evaluation
Rahul Jha, Catherine Finegan-Dollak, Ben King, Reed Coke, and Dragomir Radev. 2015 · 2015
Earlier work this paper cites.
Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the prospero registry
Rohit Borah, Andrew W Brown, Patrice L Capers, and Kathryn A Kaiser. 2017 · 2017
Earlier work this paper cites.
ScispaCy: Fast and robust models for biomedical natural language processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 2019
Earlier work this paper cites.
Automatic generation of citation texts in scholarly papers: A pilot study
Xinyu Xing, Xiaosheng Fan, and Xiaojun Wan. 2020 · 2020
Earlier work this paper cites.
MSˆ2: Multi-document summarization of medical studies
Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, and Lucy Lu Wang. 2021 · 2021
Cited alongside, same era.
BACO: A background knowledge- and content-based framework for citing sentence generation
Yubin Ge, Ly Dinh, Xiaofeng Liu, Jinsong Su, Ziyao Lu, Ante Wang, and Jana Diesner. 2021 · 2021
Cited alongside, same era.
Explaining relationships between scientific documents
Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A. Smith. 2021 · 2021
Cited alongside, same era.
Generating (factual?) narrative summaries of rcts: Experiments with neural multi-document summarization
Byron C. Wallace, Sayantani Saha, Frank Soboczenski, and Iain James Marshall. 2020 · 2021
Cited alongside, same era.
Towards generating citation sentences for multiple references with intent control
Jia-Yan Wu, Alexander Te-Wei Shieh, Shih-Ju Hsu, and Yun-Nung Chen. 2021 · 2021
Cited alongside, same era.
Scirepeval: A multi-format benchmark for scientific document representations
Amanpreet Singh, Mike D’Arcy, Arman Cohan, Doug Downey, and Sergey Feldman. 2022 · 2022
Later among the works it cites.
Multi-document scientific summarization from a knowledge graph-centric view
Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, and Ting Wang. 2022 · 2022
Later among the works it cites.
Lost in the middle: How language models use long contexts
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
Later among the works it cites.
Topicgpt: A prompt-based topic modeling framework
Chau Minh Pham, Alexander Miserlis Hoyle, Simeng Sun, and Mohit Iyyer. 2023 · 2023
Later among the works it cites.
Large language models enable few-shot clustering
Vijay Viswanathan, Kiril Gashteovski, Carolin (Haas) Lawrence, Tongshuang Sherry Wu, and Graham Neubig. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Automatic summarization of scientific articles: A survey
Nouf Ibrahim Altmami and Mohamed El Bachir Menai. 2022 · 2022
Cited alongside, same era.
Three gaps in computational text analysis methods for social sciences: A research agenda
Christian Baden, Christian Pipal, Martijn Schoonvelde, and Mariken AC G van der Velden. 2022 · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Huai hsin Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Generating a related work section for scientific papers: an optimized approach with adopting problem and method information
Pengcheng Li, Wei Lu, and Qikai Cheng. 2022 · 2022
Cited alongside, same era.
Generating a structured summary of numerous academic papers: Dataset and method
Shuaiqi Liu, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen. 2022 · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou. 2022a
Cited in the paper.
Later among the works it cites.
Goal-driven explainable clustering via language descriptions
Zihan Wang, Jingbo Shang, and Ruiqi Zhong. 2023 · 2023
Later among the works it cites.
Appraising the potential uses and harms of LLMs for medical systematic reviews
Hye Yun, Iain Marshall, Thomas Trikalinos, and Byron Wallace. 2023 · 2023
Later among the works it cites.
Clusterllm: Large language models as a guide for text clustering
Yuwei Zhang, Zihan Wang, and Jingbo Shang. 2023 · 2023
Later among the works it cites.
Hierarchical catalogue generation for literature review: A benchmark
Kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu, and Bing Qin. 2023 · 2023
Later among the works it cites.
Marg: Multi-agent review generation for scientific papers
Mike D’Arcy, Tom Hope, Larry Birnbaum, and Doug Downey. 2024 · 2024
Closest in time.
Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli
Moritz Laurer, Wouter van Atteveldt, Andreu Casas, and Kasper Welbers. 2024 · 2024
Closest in time.