117 citations · 133 across the 24 of their papers we have counts for
26 papers
Generalizable Model Editing Could Be More Than Direct Generalization
Xiaotian Ye, Chunyao Yang, Xiaohan Wang +4
A central desideratum in model editing is generalization to complex tasks, which prior work formalizes under a direct generalization paradigm: treating and evaluating LLM behavior…
GuardianBench: A Same-Scene Instruction-Contrastive Benchmark for Latent Contextual Risk in Embodied AI
Zhesheng Zhang, Jiahao Lu, Wei Liu +8
In embodied AI, safety risk can be latent: a benign instruction and a safe scene become hazardous only when composed. Prior work has advanced embodied safety by varying visual cont…
CRANE: Knowledge Editing for Reasoning MLLMs
Han Huang, Hao Wang, Mengqi Zhang +3
The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers, has introduced a new challen…
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
Xiaotian Ye, Xiaohan Wang, Mengqi Zhang +1
Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fictitious knowledge in place o…
Uncovering Entity Identity Confusion in Multimodal Knowledge Editing
Shu Wu, Xiaotian Ye, Xinyu Mou +3
Multimodal knowledge editing (MKE) aims to correct the internal knowledge of large vision-language models after deployment, yet the behavioral patterns of post-edit models remain u…
Uncovering Context Reliance in Unstructured Knowledge Editing
Zisheng Zhou, Mengqi Zhang, Shiguang Wu +4
Editing Large language models (LLMs) with real-world, unstructured knowledge is essential for correcting and updating their internal parametric knowledge. In this work, we revisit…
Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering
Fangan Dong, Zuming Yan, Xuri Ge +7
Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires post-training or computationall…
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse
Chi Zhang, Mengqi Zhang, Xiaotian Ye +5
Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for parameter-modifying methods. Existing appr…
DiffuGR: Generative Document Retrieval with Diffusion Language Models
Xinpeng Zhao, Zhaochun Ren, Yukun Zhao +9
Generative retrieval (GR) reframes document retrieval as an end-to-end task of generating sequential document identifiers (DocIDs). Existing GR methods predominantly rely on left-t…
LLM Unlearning Should Be Form-Independent
Xiaotian Ye, Mengqi Zhang, Shu Wu
Large Language Model (LLM) unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent m…
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Shiguang Wu, Zhaochun Ren, Xin Xin +5
Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless…
Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms
Xiaotian Ye, Mengqi Zhang, Shu Wu
Knowledge is fundamental to the overall capabilities of Large Language Models (LLMs). The knowledge paradigm of a model, which dictates how it encodes and utilizes knowledge, signi…
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
Wenyu Wang, Mengqi Zhang, Xiaotian Ye +3
Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information…
Uncovering Overfitting in Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods oft…
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Mengqi Zhang, Bowen Fang, Qiang Liu +4
Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…
Learning Domain-Invariant Features for Out-of-Context News Detection
Yimeng Gu, Mengqi Zhang, Ignacio Castro +2
Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Existing out-of-context news d…
Generative Retrieval as Multi-Vector Dense Retrieval
Shiguang Wu, Wenda Wei, Mengqi Zhang +5
Generative retrieval generates identifiers of relevant documents in an end-to-end manner using a sequence-to-sequence architecture for a given query. The relation between generativ…
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Jiyuan Yang, Yuanzi Li, Jingyu Zhao +8
Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly en…
Knowledge Graph Enhanced Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing…
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
Pengjie Ren, Chengshun Shi, Shiguang Wu +5
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increa…
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
Yuwei Xia, Mengqi Zhang, Qiang Liu +2
Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Mo…
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studi…
Deep Contrastive Multiview Network Embedding
Mengqi Zhang, Yanqiao Zhu, Qiang Liu +2
Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive lea…
Dynamic Graph Neural Networks for Sequential Recommendation
Mengqi Zhang, Shu Wu, Xueli Yu +2
Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventi…