:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Chen, Juan, Bi, Baolong, Zhang, Wei, Sui, Jingyan, Zhu, Xiaofei, Wang, Yuanzhuo, Mei, Lingrui, Liu, Shenghua
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	https://arxiv.org/abs/2507.01281
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation
by: Yao, Jiayu, et al.
Published: (2025)

SLANG: New Concept Comprehension of Large Language Models
by: Mei, Lingrui, et al.
Published: (2024)

a1: Steep Test-time Scaling Law via Environment Augmented Generation
by: Mei, Lingrui, et al.
Published: (2025)

Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts
by: Bi, Baolong, et al.
Published: (2024)

HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router
by: Mei, Lingrui, et al.
Published: (2024)

LPNL: Scalable Link Prediction with Large Language Models
by: Bi, Baolong, et al.
Published: (2024)

"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak
by: Mei, Lingrui, et al.
Published: (2024)

Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities
by: Bi, Baolong, et al.
Published: (2024)

Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models
by: Bi, Baolong, et al.
Published: (2025)

StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
by: Bi, Baolong, et al.
Published: (2024)

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
by: Yao, Jiayu, et al.
Published: (2025)

Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
by: Bi, Baolong, et al.
Published: (2024)

Prism-$Δ$: Differential Subspace Steering for Prompt Highlighting in Large Language Models
by: Ge, Yuyao, et al.
Published: (2026)

Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation
by: Gao, Linfeng, et al.
Published: (2025)

Gated Differentiable Working Memory for Long-Context Language Modeling
by: Mei, Lingrui, et al.
Published: (2026)

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs
by: Bi, Baolong, et al.
Published: (2025)

Retrieval-Augmented Generation with Conflicting Evidence
by: Wang, Han, et al.
Published: (2025)

Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking
by: Ge, Yuyao, et al.
Published: (2025)

Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
by: Tan, Hexiang, et al.
Published: (2024)

TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
by: Zhou, Huichi, et al.
Published: (2025)

Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation
by: Wu, Di, et al.
Published: (2024)

Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models
by: Xu, Yilong, et al.
Published: (2025)

Context-DPO: Aligning Language Models for Context-Faithfulness
by: Bi, Baolong, et al.
Published: (2024)

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
by: Ge, Yuyao, et al.
Published: (2025)

After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in Retrieval-Augmented Generation
by: Dai, Xinbang, et al.
Published: (2025)

Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning
by: Zhang, Zongmeng, et al.
Published: (2024)

Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?
by: Ge, Yuyao, et al.
Published: (2024)

A Survey of Context Engineering for Large Language Models
by: Mei, Lingrui, et al.
Published: (2025)

Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
by: Sun, Jiashuo, et al.
Published: (2026)

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
by: Wang, Chenyu, et al.
Published: (2026)

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
by: Zhou, Yujia, et al.
Published: (2024)

Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs
by: Ge, Ziyu, et al.
Published: (2025)

Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning
by: Dong, Qi, et al.
Published: (2026)

Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
by: Yue, Zhenrui, et al.
Published: (2024)

ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation
by: Niu, Qingying, et al.
Published: (2026)

CtrlRAG: Black-box Document Poisoning Attacks for Retrieval-Augmented Generation of Large Language Models
by: Sui, Runqi
Published: (2025)

Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
by: He, Bolei, et al.
Published: (2024)

Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
by: Choi, Eunseong, et al.
Published: (2025)

Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
by: Tang, Minghao, et al.
Published: (2025)

Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception
by: Ni, Shiyu, et al.
Published: (2025)