43 citations · 75 across the 7 of their papers we have counts for
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cs.CL2024
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov +9
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccura…
cs.LG2024
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
David Thulke, Yingbo Gao, Petrus Pelser +23
This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models fro…