984 citations · 1.1k across the 27 of their papers we have counts for
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RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen +9
Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing ap…
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages
Mingda Chen, Kevin Heffernan, Onur Çelebi +2
We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, this improved proxy leverages rule…
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering
Mingda Chen, Xilun Chen, Wen-tau Yih
Few-shot learning for open domain multi-hop question answering typically relies on the incontext learning capability of large language models (LLMs). While powerful, these LLMs usu…