306 citations · 415 across the 10 of their papers we have counts for
13 papers
Sources of Hallucination by Large Language Models on Inference Tasks
Nick McKenna, Tianyi Li, Liang Cheng +3
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a seri…
Smoothing Entailment Graphs with Language Models
Nick McKenna, Tianyi Li, Mark Johnson +1
The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by Open Relation Extraction (ORE). EGs…
Blindness to Modality Helps Entailment Graph Mining
Liane Guillou, Sander Bijl de Vroe, Mark Johnson +1
Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also b…
Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini +2
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct even…
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Yufei Wang, Can Xu, Huang Hu +5
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language gen…
There and Back Again: Self-supervised Multispectral Correspondence Estimation
Celyn Walters, Oscar Mendez, Mark Johnson +1
Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in colo…