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Found 9 papers, 2 papers with code
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration
However, the performance of in-context learning is susceptible to the choice of prompt format, training examples and the ordering of the training examples.
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments
Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input.
Program Enhanced Fact Verification with Verbalization and Graph Attention Network
Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision.
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation
Data-to-text generation aims to generate descriptions given a structured input data (i. e., a table with multiple records).
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation
Recent neural language generation systems often \textit{hallucinate} contents (i. e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Aggregated Semantic Matching for Short Text Entity Linking
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Incorporating Consistency Verification into Neural Data-to-Document Generation
Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.
