24 citations · 80 across the 7 of their papers we have counts for
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cs.CL2023★ 24 cited
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
cs.CL2022★ 1 cited
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness
Sicong Huang, Asli Celikyilmaz, Haoran Li
Abstractive summarization models typically generate content unfaithful to the input, thus highlighting the significance of evaluating the faithfulness of generated summaries. Most…