Failures Pave the Way: Enhancing Large Language Models through
Tuning-free Rule Accumulation

Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation

24 October 2023

Peng Li

Papers citing "Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation"

7 / 7 papers shown

Title
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting Emmanuel Aboah Boateng Cassiano O. Becker Nabiha Asghar Kabir Walia Ashwin Srinivasan Ehi Nosakhare Victor Dibia Soundar Srinivasan LRM 28 0 0 18 Aug 2024
A Survey on Self-Evolution of Large Language Models Zhengwei Tao Ting-En Lin Xiancai Chen Hangyu Li Yuchuan Wu Yongbin Li Zhi Jin Fei Huang Dacheng Tao Jingren Zhou LRM LM&Ro 43 21 0 22 Apr 2024
Towards Unified Alignment Between Agents, Humans, and Environment Zonghan Yang An Liu Zijun Liu Kai Liu Fangzhou Xiong ... Zhenhe Zhang Fuwen Luo Zhicheng Guo Peng Li Yang Liu 16 4 0 12 Feb 2024
Semiparametric Language Models Are Scalable Continual Learners Guangyue Peng Tao Ge Si-Qing Chen Furu Wei Houfeng Wang KELM 34 10 0 02 Mar 2023
Improving alignment of dialogue agents via targeted human judgements Amelia Glaese Nat McAleese Maja Trkebacz John Aslanides Vlad Firoiu ... John F. J. Mellor Demis Hassabis Koray Kavukcuoglu Lisa Anne Hendricks G. Irving ALM AAML 222 495 0 28 Sep 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 291 2,712 0 24 May 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022