750 citations · 813 across the 12 of their papers we have counts for
12 papers
ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction
Bojun Zhang, Junhong Liang, Feifei Zhai +2
Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this paper, we introduce ReViCo…
A Large-Scale Multi-Dimensional Empirical Study of LLMs for Conversation Summarization
Weixiao Zhou, Gengyao Li, Junnan Zhu +3
Despite the significant advancement of LLMs in conversation summarization, their evaluation remains limited by insufficient scenarios, input lengths, and sample sizes. Furthermore,…
NeuroArmor: Safe-Variant-Guided Representation Consistency for Selective Re-Anchoring in Jailbreak Defense
Zhongyang Lin, Ziran Zhao, Feifei Zhai +1
Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translation, encoding, adversarial suffi…
CROP: Contextual Region-Oriented Visual Token Pruning
Jiawei Guo, Feifei Zhai, Pu Jian +2
Current VLM-based VQA methods often process entire images, leading to excessive visual tokens that include redundant information irrelevant to the posed question. This abundance of…
What Are They Talking About? A Benchmark of Knowledge-Grounded Discussion Summarization
Weixiao Zhou, Junnan Zhu, Gengyao Li +4
Traditional dialogue summarization primarily focuses on dialogue content, assuming it comprises adequate information for a clear summary. However, this assumption often fails for d…
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification
Junnan Zhu, Min Xiao, Yining Wang +3
LLMs have achieved remarkable fluency and coherence in text generation, yet their widespread adoption has raised concerns about content reliability and accountability. In high-stak…
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Weixiao Zhou, Gengyao Li, Xianfu Cheng +4
Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a…
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
Yang Zhao, Junnan Zhu, Lu Xiang +4
A common scenario of Multilingual Neural Machine Translation (MNMT) is that each translation task arrives in a sequential manner, and the training data of previous tasks is unavail…
Neural Machine Translation with Explicit Phrase Alignment
Jiacheng Zhang, Huanbo Luan, Maosong Sun +3
While neural machine translation (NMT) has achieved state-of-the-art translation performance, it is unable to capture the alignment between the input and output during the translat…
Improving the Transformer Translation Model with Document-Level Context
Jiacheng Zhang, Huanbo Luan, Maosong Sun +4
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to…
Neural Models for Sequence Chunking
Feifei Zhai, Saloni Potdar, Bing Xiang +1
Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the mea…
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Ramesh Nallapati, Feifei Zhai, Bowen Zhou
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or compara…