Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution
Fuente:
arXiv
Saved in:
| Main Authors: | Qin, Tian, Chen, Junzhe, Shi, Yuqing, Zhang, Tianshu, Ju, Qiang, Wen, Lijie |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models
by: Chen, Junzhe, et al.
Published: (2024)
by: Chen, Junzhe, et al.
Published: (2024)
Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey
by: Zhao, Yuqing, et al.
Published: (2026)
by: Zhao, Yuqing, et al.
Published: (2026)
MambaOut: Do We Really Need Mamba for Vision?
by: Yu, Weihao, et al.
Published: (2024)
by: Yu, Weihao, et al.
Published: (2024)
Do We Really Need a Large Number of Visual Prompts?
by: Kim, Youngeun, et al.
Published: (2023)
by: Kim, Youngeun, et al.
Published: (2023)
PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems
by: Pennas, Panagiotis Georgios, et al.
Published: (2026)
by: Pennas, Panagiotis Georgios, et al.
Published: (2026)
Do We Really Need to Design New Byzantine-robust Aggregation Rules?
by: Fang, Minghong, et al.
Published: (2025)
by: Fang, Minghong, et al.
Published: (2025)
Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations
by: Zhou, Li, et al.
Published: (2025)
by: Zhou, Li, et al.
Published: (2025)
Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?
by: Lyu, Yunbo, et al.
Published: (2025)
by: Lyu, Yunbo, et al.
Published: (2025)
Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models
by: Zhang, Huixuan, et al.
Published: (2024)
by: Zhang, Huixuan, et al.
Published: (2024)
Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
by: Attimonelli, Matteo, et al.
Published: (2025)
by: Attimonelli, Matteo, et al.
Published: (2025)
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
by: Vhaduri, Sudip, et al.
Published: (2026)
by: Vhaduri, Sudip, et al.
Published: (2026)
Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge
by: Sui, Yi, et al.
Published: (2025)
by: Sui, Yi, et al.
Published: (2025)
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
by: Wang, Yanbo, et al.
Published: (2025)
by: Wang, Yanbo, et al.
Published: (2025)
Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement
by: Qin, Zhenxin, et al.
Published: (2026)
by: Qin, Zhenxin, et al.
Published: (2026)
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
by: Zhao, Zhonghan, et al.
Published: (2024)
by: Zhao, Zhonghan, et al.
Published: (2024)
Do We Need Tensor Cores for Stencil Computations?
by: Gu, Qiqi, et al.
Published: (2026)
by: Gu, Qiqi, et al.
Published: (2026)
When Do We Not Need Larger Vision Models?
by: Shi, Baifeng, et al.
Published: (2024)
by: Shi, Baifeng, et al.
Published: (2024)
Do We Need Subsidiarity in Software?
by: Conwill, Louisa, et al.
Published: (2025)
by: Conwill, Louisa, et al.
Published: (2025)
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
by: You, Liangliang, et al.
Published: (2025)
by: You, Liangliang, et al.
Published: (2025)
Do GPUs Really Need New Tabular File Formats?
by: Luo, Jigao, et al.
Published: (2026)
by: Luo, Jigao, et al.
Published: (2026)
Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
by: Liu, Xiaoze, et al.
Published: (2026)
by: Liu, Xiaoze, et al.
Published: (2026)
SIRA: Scalable Inter-frame Relation and Association for Radar Perception
by: Yataka, Ryoma, et al.
Published: (2024)
by: Yataka, Ryoma, et al.
Published: (2024)
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
by: Liu, Zikang, et al.
Published: (2025)
by: Liu, Zikang, et al.
Published: (2025)
What Do We Need for an Agentic Society?
by: Ko, Kwon, et al.
Published: (2026)
by: Ko, Kwon, et al.
Published: (2026)
Can Knowledge Editing Really Correct Hallucinations?
by: Huang, Baixiang, et al.
Published: (2024)
by: Huang, Baixiang, et al.
Published: (2024)
Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding
by: Wang, Chao, et al.
Published: (2025)
by: Wang, Chao, et al.
Published: (2025)
What Do Indonesians Really Need from Language Technology? A Nationwide Survey
by: Kautsar, Muhammad Dehan Al, et al.
Published: (2025)
by: Kautsar, Muhammad Dehan Al, et al.
Published: (2025)
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?
by: Ray, Sushant Kumar, et al.
Published: (2026)
by: Ray, Sushant Kumar, et al.
Published: (2026)
Accelerating Direct Preference Optimization with Prefix Sharing
by: Wang, Franklin, et al.
Published: (2024)
by: Wang, Franklin, et al.
Published: (2024)
Tracing and Mitigating Hallucinations in Multimodal LLMs via Dynamic Attention Localization
by: Yang, Tiancheng, et al.
Published: (2025)
by: Yang, Tiancheng, et al.
Published: (2025)
ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
by: Kim, Minchan, et al.
Published: (2024)
by: Kim, Minchan, et al.
Published: (2024)
Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
by: Barkley, Liam, et al.
Published: (2024)
by: Barkley, Liam, et al.
Published: (2024)
Cognitive Tools for Understanding History: What More Do We Need?
by: O'neill, D. K., et al.
Published: (2006)
by: O'neill, D. K., et al.
Published: (2006)
Do We Really Even Need Data?
by: Hoffman, Kentaro, et al.
Published: (2024)
by: Hoffman, Kentaro, et al.
Published: (2024)
Do Vision Encoders Truly Explain Object Hallucination?: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore
by: Oh, Hongseok, et al.
Published: (2025)
by: Oh, Hongseok, et al.
Published: (2025)
Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
by: Yue, Zihao, et al.
Published: (2024)
by: Yue, Zihao, et al.
Published: (2024)
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
by: Dang, Tiantian, et al.
Published: (2026)
by: Dang, Tiantian, et al.
Published: (2026)
Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
by: Tan, Zhijie, et al.
Published: (2025)
by: Tan, Zhijie, et al.
Published: (2025)
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation
by: Wang, Ao, et al.
Published: (2024)
by: Wang, Ao, et al.
Published: (2024)
Do MLLMs Really Understand the Charts?
by: Zhang, Xiao, et al.
Published: (2025)
by: Zhang, Xiao, et al.
Published: (2025)
Similar Items
-
ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models
by: Chen, Junzhe, et al.
Published: (2024) -
Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey
by: Zhao, Yuqing, et al.
Published: (2026) -
MambaOut: Do We Really Need Mamba for Vision?
by: Yu, Weihao, et al.
Published: (2024) -
Do We Really Need a Large Number of Visual Prompts?
by: Kim, Youngeun, et al.
Published: (2023) -
PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems
by: Pennas, Panagiotis Georgios, et al.
Published: (2026)