Abstraction-of-Thought Makes Language Models Better Reasoners

Abstraction-of-Thought Makes Language Models Better Reasoners

18 June 2024

Hongming Zhang

Changshui Zhang

Papers citing "Abstraction-of-Thought Makes Language Models Better Reasoners"

5 / 5 papers shown

Title
Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions Yiming Du Wenyu Huang Danna Zheng Zhaowei Wang Sébastien Montella Mirella Lapata Kam-Fai Wong Jeff Z. Pan KELM MU 78 2 0 01 May 2025
ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty Qing Zong Z. Wang Tianshi Zheng Xiyu Ren Y. Song 62 1 0 31 Dec 2024
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Lokesh Nagalapatti Chun-Liang Li Chih-Kuan Yeh Hootan Nakhost Yasuhisa Fujii Alexander Ratner Ranjay Krishna Chen-Yu Lee Tomas Pfister ALM 217 499 0 03 May 2023
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 322 4,077 0 24 May 2022
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,821 0 17 Sep 2019