51 citations · 64 across the 17 of their papers we have counts for
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Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Qiming Bao, Gael Gendron, Alex Yuxuan Peng +5
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-li…
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Qiming Bao, Juho Leinonen, Alex Yuxuan Peng +7
Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing e…
Large Language Models Are Not Strong Abstract Reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock +1
Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However,…
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng +10
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical re…
Input-length-shortening and text generation via attention values
Neşet Özkan Tan, Alex Yuxuan Peng, Joshua Bensemann +4
Identifying words that impact a task's performance more than others is a challenge in natural language processing. Transformers models have recently addressed this issue by incorpo…