2 citations · 5 across the 21 of their papers we have counts for
27 papers
PVFH-Bench: Benchmarking Premise-Induced Visual Fixation Hallucinations in Vision-Language Models
Xinhao Wang, Junbo Cui, Zhenghao Liu +4
Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains a persistent challenge, underm…
REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning
Zhensheng Jin, Xin Dai, Zhenghao Liu +5
Large Language Models (LLMs) increasingly leverage long-form reasoning to solve complex tasks, yet their reasoning processes can deviate from the provided context when evidence is…
SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation
Ruochang Li, Pengcheng Huang, Zhenghao Liu +5
Retrieval-augmented generation (RAG) enhances the faithfulness of large language models (LLMs) by incorporating external knowledge into the generation process. However, conflicts b…
Memory Shot for Long-Term Dialogue
Chunyi Peng, Haidong Xin, Xuanshuo Sheng +7
Large Language Models (LLMs) have demonstrated strong capabilities in general conversation, instruction following, and complex reasoning. However, in long-term dialogue settings, t…
ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment
Hao Yang, Yifan Ji, Zhipeng Xu +6
Visual document retrieval aims to retrieve a set of document pages relevant to a query from visually rich collections. Existing methods often employ Vision-Language Models (VLMs) t…
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains
Yuqi Xiong, Chunyi Peng, Zhipeng Xu +6
Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks us…
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization
Haidong Xin, Xinze Li, Zhenghao Liu +6
Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. How…
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents
Yifan Ji, Zhipeng Xu, Zhenghao Liu +7
Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual quality, and task-specific information req…
Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction
Xingjie Gao, Pengcheng Huang, Zhenghao Liu +6
Equipping Large Language Models (LLMs) with external tools enables them to solve complex real-world problems. However, the robustness of existing methods remains a critical challen…
Long-Chain Reasoning Distillation via Adaptive Prefix Alignment
Zhenghao Liu, Zhuoyang Wu, Xinze Li +6
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in solving complex mathematical problems. Recent studies show that distilling long re…
Revealing the Attention Floating Mechanism in Masked Diffusion Models
Xin Dai, Pengcheng Huang, Zhenghao Liu +6
Masked diffusion models (MDMs), which leverage bidirectional attention and a denoising process, are narrowing the performance gap with autoregressive models (ARMs). However, their…
Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Shuliang Liu, Zhipeng Xu, Zhenghao Liu +6
Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LL…
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
Shaohua Duan, Pengcheng Huang, Xinze Li +7
Long-context modeling is critical for a wide range of real-world tasks, including long-context question answering, summarization, and complex reasoning tasks. Recent studies have e…
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection
Zhuoyang Wu, Xinze Li, Zhenghao Liu +7
Large Language Models (LLMs) have exhibited strong reasoning capabilities and achieved remarkable performance in mathematical problem-solving tasks. Recently, distilling reasoning…
LISRec: Modeling User Preferences with Learned Item Shortcuts for Sequential Recommendation
Haidong Xin, Zhenghao Liu, Sen Mei +7
User-item interaction histories are pivotal for sequential recommendation systems but often include noise, such as unintended clicks or actions that fail to reflect genuine user pr…
Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation
Chunyi Peng, Zhipeng Xu, Zhenghao Liu +7
Multimodal Retrieval-Augmented Generation (MRAG) has shown promise in mitigating hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge. How…
Enhancing the Patent Matching Capability of Large Language Models via the Memory Graph
Qiushi Xiong, Zhipeng Xu, Zhenghao Liu +6
Intellectual Property (IP) management involves strategically protecting and utilizing intellectual assets to enhance organizational innovation, competitiveness, and value creation.…
RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
Mingyan Wu, Zhenghao Liu, Yukun Yan +5
Retrieval-Augmented Generation (RAG) enhances the performance of Large Language Models (LLMs) by incorporating external knowledge. However, LLMs still encounter challenges in effec…
HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization
Haolan Wang, Zhenghao Liu, Xinze Li +7
Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information. Recent methods employ Multi-modal Large Language Models (MLLMs)…
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Sijia Yao, Pengcheng Huang, Zhenghao Liu +4
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the r…
Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking
Yifan Ji, Zhipeng Xu, Zhenghao Liu +7
Recent dense retrievers increasingly leverage the robust text understanding capabilities of Large Language Models (LLMs), encoding queries and documents into a shared embedding spa…
Building A Coding Assistant via the Retrieval-Augmented Language Model
Xinze Li, Hanbin Wang, Zhenghao Liu +6
Pretrained language models have shown strong effectiveness in code-related tasks, such as code retrieval, code generation, code summarization, and code completion tasks. In this pa…
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
Weiqing Yang, Hanbin Wang, Zhenghao Liu +7
Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human d…
WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models
Wenlong Zhao, Debanjan Mondal, Niket Tandon +3
The awareness of multi-cultural human values is critical to the ability of language models (LMs) to generate safe and personalized responses. However, this awareness of LMs has bee…