52 citations · 104 across the 7 of their papers we have counts for
11 papers · 1 filter
Query Refinement Prompts for Closed-Book Long-Form Question Answering
Reinald Kim Amplayo, Kellie Webster, Michael Collins +2
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be…
Calibrating Sequence likelihood Improves Conditional Language Generation
Yao Zhao, Misha Khalman, Rishabh Joshi +3
Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained mod…
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
Shashi Narayan, Gonçalo Simões, Yao Zhao +4
We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding str…
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
Xinnuo Xu, Ondřej Dušek, Shashi Narayan +2
One of the most challenging aspects of current single-document news summarization is that the summary often contains 'extrinsic hallucinations', i.e., facts that are not present in…
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Rahul Aralikatte, Shashi Narayan, Joshua Maynez +2
Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously…
Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan, Yao Zhao, Joshua Maynez +3
We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries…