June 6, 2024, 4:51 a.m. | Jinghan Zhang, Xiting Wang, Weijieying Ren, Lu Jiang, Dongjie Wang, Kunpeng Liu

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.02746v1 Announce Type: new
Abstract: Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Augmented Thoughts often fall short in complex tasks due to the limitations of insufficient local retrieval of factual knowledge and inadequate global selection of strategies. These limitations make it challenging for these methods to balance factual accuracy and comprehensive logical optimization effectively. To address these limitations, we introduce the Retrieval Augmented Thought …

abstract arxiv capabilities cs.cl decision global however knowledge language language models large language large language models limitations llms making reasoning retrieval tasks thought thoughts tree type

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