no code implementations • 22 May 2024 • Qiji Zhou, Ruochen Zhou, Zike Hu, Panzhong Lu, Siyang Gao, Yue Zhang
Recent advancements in Chain-of-Thought (CoT) and related rationale-based works have significantly improved the performance of Large Language Models (LLMs) in complex reasoning tasks.
1 code implementation • 13 Oct 2023 • Hanmeng Liu, Zhiyang Teng, Ruoxi Ning, Jian Liu, Qiji Zhou, Yue Zhang
Recently, large language models (LLMs), including notable models such as GPT-4 and burgeoning community models, have showcased significant general language understanding abilities.
1 code implementation • 20 May 2023 • Hanmeng Liu, Zhiyang Teng, Leyang Cui, Chaoli Zhang, Qiji Zhou, Yue Zhang
LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
1 code implementation • 7 Apr 2023 • Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, Yue Zhang
With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks.
no code implementations • ACL 2020 • Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou
The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa.
no code implementations • ACL 2020 • Qiji Zhou, Yue Zhang, Donghong Ji, Hao Tang
Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences.