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Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts
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In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures.
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Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts
A new Russian poetry dataset and benchmark show that supervised classifiers trained on synthetic distortions detect grammatical defects (F0.5=0.86) while unsupervised and zero-shot methods perform near random.
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