Content-based diversity metrics, such as Vendi Score and Chamfer distance, agree with LLM-based diversity ratings far better than form-based metrics like self-BLEU across three commonsense generation datasets.
Recent Advances in Neural Question Generation
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abstract
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.
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Evaluating the Evaluation of Diversity in Commonsense Generation
Content-based diversity metrics, such as Vendi Score and Chamfer distance, agree with LLM-based diversity ratings far better than form-based metrics like self-BLEU across three commonsense generation datasets.