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cs.CL2023★ 1 cited
Bias in News Summarization: Measures, Pitfalls and Corpora
Julius Steen, Katja Markert
Summarization is an important application of large language models (LLMs). Most previous evaluation of summarization models has focused on their content selection, faithfulness, gr…
cs.CL2023★ 2 cited
AMR4NLI: Interpretable and robust NLI measures from semantic graphs
Juri Opitz, Shira Wein, Julius Steen +2
The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but t…
cs.CL2023
With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness
Julius Steen, Juri Opitz, Anette Frank +1
Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as…