Papers by Mingzhai Sun

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Challenge:	Current large language models (LLMs) have shown a powerful ability of reasoning in understanding question requirement, retrieving the supporting fact and generating a precise answer.
Approach:	They propose a brain-inspired Theta-Gamma hierarchical oscillatory reasoning framework which decouples attention between global planning and local retrieval.
Outcome:	Extensive comparative experiments and specific validation experiments on multi-hop QA benchmarks show that THOR improves answer accuracy and robustness while mitigating limitations.