Výsledky vyhledávání - "Li, Peiguang"

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LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation

Autor: Wang, Keheng, Duan, Feiyu, Li, Peiguang, Wang, Sirui, Cai, Xunliang

Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RAG in understanding complex mul

Externí odkaz: http://arxiv.org/abs/2404.14043

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Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model

Autor: Wang, Sirui, Li, Peiguang, Xian, Yunsen, Zhang, Hongzhi

The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence. However, inevitable random action, i.e. user randomly accesses an item among multiple candidates or clicks several

Externí odkaz: http://arxiv.org/abs/2404.05342

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Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering

Autor: Wang, Keheng, Duan, Feiyu, Wang, Sirui, Li, Peiguang, Xian, Yunsen, Yin, Chuantao, Rong, Wenge, Xiong, Zhang

Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and the inability to access external knowledge, LLMs often come with incorr

Externí odkaz: http://arxiv.org/abs/2308.13259

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