1 citations · 2 across the 5 of their papers we have counts for
5 papers
Domain-Specialized Tree of Thought through Plug-and-Play Predictors
Xuanqi Gao, Haoyu Wang, Jun Sun +2
While Large Language Models (LLMs) have advanced complex reasoning, prominent methods like the Tree of Thoughts (ToT) framework face a critical trade-off between exploration depth…
Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final ans…
Large Language Models Can Self-Correct with Key Condition Verification
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang +3
Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback. Unfortunately, the study concluded…
Instructing Large Language Models to Identify and Ignore Irrelevant Conditions
Zhenyu Wu, Chao Shen, Meng Jiang
Math word problem (MWP) solving requires generating a reasoning path based on a given problem description that often contains irrelevant conditions. Existing chain-of-thought (CoT)…
Get an A in Math: Progressive Rectification Prompting
Zhenyu Wu, Meng Jiang, Chao Shen
Chain-of-Thought (CoT) prompting methods have enabled large language models (LLMs) to generate reasoning paths and solve math word problems (MWPs). However, they are sensitive to m…