no code implementations • 2 Oct 2023 • Tianci Xue, Ziqi Wang, Yixia Li, Yun Chen, Guanhua Chen
To alleviate the illusion of competence of models, we first ask the model to verify the correctness of shown examples.
no code implementations • 1 Oct 2023 • Tianci Xue, Ziqi Wang, Heng Ji
To this end, prior works incorporate controllable generations for alignment to make language models learn multiple preferences and provide outputs with different preferences during inference if asked.
no code implementations • 19 May 2023 • Tianci Xue, Ziqi Wang, Zhenhailong Wang, Chi Han, Pengfei Yu, Heng Ji
To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions.