6 citations · 15 across the 6 of their papers we have counts for
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3 papers · 1 filter
cs.CL2024★ 1 cited
Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service
Song Wang, Xun Wang, Jie Mei +6
Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that…
cs.CL2024★ 3 cited
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
Xin Cheng, Xun Wang, Xingxing Zhang +5
This paper introduces xRAG, an innovative context compression method tailored for retrieval-augmented generation. xRAG reinterprets document embeddings in dense retrieval--traditio…
cs.CL2024★ 6 cited
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…