most citedFaithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods

28 citations · 54 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV202211 cited

UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance

Wei Li, Xue Xu, Xinyan Xiao +8

Diffusion generative models have recently greatly improved the power of text-conditioned image generation. Existing image generation models mainly include text conditional diffusio…

cs.CL20222 cited

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness

Wenhao Wu, Wei Li, Jiachen Liu +4

Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the…

cs.CL20221 cited

CDConv: A Benchmark for Contradiction Detection in Chinese Conversations

Chujie Zheng, Jinfeng Zhou, Yinhe Zheng +6

Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging.…

cs.CL20229 cited

Unified Structure Generation for Universal Information Extraction

Yaojie Lu, Qing Liu, Dai Dai +5

Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation fra…

cs.CL2022

PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation

Zhe Hu, Hou Pong Chan, Jiachen Liu +3

Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that re…

cs.CV20222 cited

UNIMO-2: End-to-End Unified Vision-Language Grounded Learning

Wei Li, Can Gao, Guocheng Niu +5

Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. However, most existing methods can only learn from aligned image-cap…