4 papers
Logics-Parsing-Omni Technical Report
Xin An, Jingyi Cai, Xiangyang Chen +22
Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This fram…
Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination
Xiangyan Chen, Yujian Gan, Matthew Purver
The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses that may misle…
Logics-Parsing Technical Report
Xiangyang Chen, Shuzhao Li, Xiuwen Zhu +7
Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradi…
Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation
Xiangyan Chen, Yujian Gan, Yimeng Gu +1
Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect…