Learning to Reduce: Towards Improving Performance of Large Language
Models on Structured Data

Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data

3 July 2024

Sungchul Kim

Tong Yu

Xiang Chen

Papers citing "Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data"

6 / 6 papers shown

Title
Can Large Language Models Understand Real-World Complex Instructions? Qi He Jie Zeng Wenhao Huang Lina Chen Jin Xiao ... Shisong Chen Yikai Zhang Zhouhong Gu Jiaqing Liang Yanghua Xiao ALM LRM ELM 90 50 0 17 Sep 2023
Generate rather than Retrieve: Large Language Models are Strong Context Generators W. Yu Dan Iter Shuohang Wang Yichong Xu Mingxuan Ju Soumya Sanyal Chenguang Zhu Michael Zeng Meng-Long Jiang RALM AIMat 215 318 0 21 Sep 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 291 4,048 0 24 May 2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 297 3,163 0 21 Mar 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,261 0 28 Jan 2022
Fine-Tuning Language Models from Human Preferences Daniel M. Ziegler Nisan Stiennon Jeff Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano G. Irving ALM 275 1,561 0 18 Sep 2019