4 citations · 16 across the 10 of their papers we have counts for
10 papers
On Positional Bias of Faithfulness for Long-form Summarization
David Wan, Jesse Vig, Mohit Bansal +1
Large Language Models (LLMs) often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs. We investigate the presence of this bia…
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
David Wan, Jaemin Cho, Elias Stengel-Eskin +1
Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to at…
HistAlign: Improving Context Dependency in Language Generation by Aligning with History
David Wan, Shiyue Zhang, Mohit Bansal
Language models (LMs) can generate hallucinations and incoherent outputs, which highlights their weak context dependency. Cache-LMs, which augment LMs with a memory of recent histo…
Faithfulness-Aware Decoding Strategies for Abstractive Summarization
David Wan, Mengwen Liu, Kathleen McKeown +2
Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied.…
Evaluating and Improving Factuality in Multimodal Abstractive Summarization
David Wan, Mohit Bansal
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modalit…
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization
Shiyue Zhang, David Wan, Mohit Bansal
The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithf…