1 citations · 3 across the 6 of their papers we have counts for
6 papers
ADRD-Bench: A Preliminary LLM Benchmark for Alzheimer's Disease and Related Dementias
Guangxin Zhao, Jiahao Zheng, Malaz Boustani +4
Large language models (LLMs) have shown great potential for healthcare applications. However, existing evaluation benchmarks provide minimal coverage of Alzheimer's Disease and Rel…
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…
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +3
Taxonomies play a crucial role in various applications by providing a structural representation of knowledge. The task of taxonomy expansion involves integrating emerging concepts…
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…