8 citations · 12 across the 16 of their papers we have counts for
16 papers
Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms
Yueyan Li, Haibo Wang, Caixia Yuan +1
Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing solutions rely on external…
BARD: budget-aware reasoning distillation
Lujie Niu, Lei Shen, Yi Jiang +4
While long Chain-of-Thought (CoT) distillation effectively transfers reasoning capability to smaller language models, the reasoning process often remains redundant and computationa…
Reading Images Like Texts: Sequential Image Understanding in Vision-Language Models
Yueyan Li, Chenggong Zhao, Zeyuan Zang +2
Vision-Language Models (VLMs) have demonstrated remarkable performance across a variety of real-world tasks. However, existing VLMs typically process visual information by serializ…
Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning
Yuheng Lu, ZiMeng Bai, Caixia Yuan +2
Large language models (LLMs) exhibit remarkable capabilities in handling natural language tasks; however, they may struggle to consistently follow complex instructions including th…
Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents
Haochen Sun, Shuwen Zhang, Lujie Niu +6
Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent Sy…
Fine-Tuning is Subgraph Search: A New Lens on Learning Dynamics
Yueyan Li, Wenhao Gao, Caixia Yuan +1
The study of mechanistic interpretability aims to reverse-engineer a model to explain its behaviors. While recent studies have focused on the static mechanism of a certain behavior…
Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models
Yuheng Lu, Bingshuo Qian, Caixia Yuan +2
Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new doma…
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
Yangyang Luo, Shiyu Tian, Caixia Yuan +1
Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effe…
A Task-oriented Dialog Model with Task-progressive and Policy-aware Pre-training
Lucen Zhong, Hengtong Lu, Caixia Yuan +4
Pre-trained conversation models (PCMs) have achieved promising progress in recent years. However, existing PCMs for Task-oriented dialog (TOD) are insufficient for capturing the se…
AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking
Di Lu, Zhongping Liang, Caixia Yuan +1
This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015. The task involves extracting named entity mentions from short search que…
An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog System
Sai Zhang, Yuwei Hu, Xiaojie Wang +1
Reinforcement learning has been applied to train the dialog systems in many works. Previous approaches divide the dialog system into multiple modules including DST (dialog state tr…
SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph
Yuxing Long, Binyuan Hui, Fulong Ye +5
Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when…
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
Sai Zhang, Yuwei Hu, Yuchuan Wu +5
A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a co…
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
Junpeng Liu, Yanyan Zou, Hainan Zhang +4
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In…
Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue
Han Liu, Caixia Yuan, Xiaojie Wang +3
We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark pr…
Label-Wise Document Pre-Training for Multi-Label Text Classification
Han Liu, Caixia Yuan, Xiaojie Wang
A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge b…