33 citations · 130 across the 9 of their papers we have counts for
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cs.CL2023
Reimagining Retrieval Augmented Language Models for Answering Queries
Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez +4
We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where mo…
cs.CL2023★ 8 cited
Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Weijia Shi, Xiaochuang Han, Mike Lewis +3
Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we pres…