16 citations · 31 across the 8 of their papers we have counts for
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cs.CL2024
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation
Zexiong Ma, Shengnan An, Zeqi Lin +3
Large language models (LLMs) are susceptible to generating hallucinated information, despite the integration of retrieval-augmented generation (RAG). Parallel context extension (PC…
cs.CL2024★ 10 cited
Make Your LLM Fully Utilize the Context
Shengnan An, Zexiong Ma, Zeqi Lin +2
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-m…
cs.CL2023★ 5 cited
Learning From Mistakes Makes LLM Better Reasoner
Shengnan An, Zexiong Ma, Zeqi Lin +3
Large language models (LLMs) recently exhibited remarkable reasoning capabilities on solving math problems. To further improve their reasoning capabilities, this work explores whet…