Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Lv, Qitan, Wang, Jie, Chen, Hanzhu, Li, Bin, Zhang, Yongdong, Wu, Feng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs
by: Chen, Hanzhu, et al.
Published: (2024)
by: Chen, Hanzhu, et al.
Published: (2024)
KALE: Enhancing Knowledge Manipulation in Large Language Models via Knowledge-aware Learning
by: Lv, Qitan, et al.
Published: (2026)
by: Lv, Qitan, et al.
Published: (2026)
Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models
by: Feng, Yujie, et al.
Published: (2026)
by: Feng, Yujie, et al.
Published: (2026)
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
by: Chen, Xinxi, et al.
Published: (2024)
by: Chen, Xinxi, et al.
Published: (2024)
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
by: Chen, Yushuo, et al.
Published: (2024)
by: Chen, Yushuo, et al.
Published: (2024)
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
by: Wang, Shengyuan, et al.
Published: (2025)
by: Wang, Shengyuan, et al.
Published: (2025)
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
by: Zhang, Hongxiang, et al.
Published: (2025)
by: Zhang, Hongxiang, et al.
Published: (2025)
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
by: Liu, Tianyu, et al.
Published: (2024)
by: Liu, Tianyu, et al.
Published: (2024)
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
by: Deng, Kunquan, et al.
Published: (2024)
by: Deng, Kunquan, et al.
Published: (2024)
Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
by: Chu, Yun-Wei, et al.
Published: (2025)
by: Chu, Yun-Wei, et al.
Published: (2025)
Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models
by: Khadangi, Afshin, et al.
Published: (2025)
by: Khadangi, Afshin, et al.
Published: (2025)
Identifying Knowledge Editing Types in Large Language Models
by: Li, Xiaopeng, et al.
Published: (2024)
by: Li, Xiaopeng, et al.
Published: (2024)
Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Models
by: Tao, Dehao, et al.
Published: (2024)
by: Tao, Dehao, et al.
Published: (2024)
Multi-Modal Fact-Verification Framework for Reducing Hallucinations in Large Language Models
by: Patel, Piyushkumar
Published: (2025)
by: Patel, Piyushkumar
Published: (2025)
Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models
by: Feng, Yijun
Published: (2025)
by: Feng, Yijun
Published: (2025)
Fine-Tuned Large Language Models for Logical Translation: Reducing Hallucinations with Lang2Logic
by: Pan, Muyu, et al.
Published: (2025)
by: Pan, Muyu, et al.
Published: (2025)
Alleviating Hallucinations of Large Language Models through Induced Hallucinations
by: Zhang, Yue, et al.
Published: (2023)
by: Zhang, Yue, et al.
Published: (2023)
Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
by: Wan, Yixin, et al.
Published: (2023)
by: Wan, Yixin, et al.
Published: (2023)
Copy-Paste to Mitigate Large Language Model Hallucinations
by: Long, Yongchao, et al.
Published: (2025)
by: Long, Yongchao, et al.
Published: (2025)
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
by: Chen, Kedi, et al.
Published: (2024)
by: Chen, Kedi, et al.
Published: (2024)
Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
by: Jiang, Chaoya, et al.
Published: (2024)
by: Jiang, Chaoya, et al.
Published: (2024)
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
by: Lyu, Yougang, et al.
Published: (2024)
by: Lyu, Yougang, et al.
Published: (2024)
ANAH: Analytical Annotation of Hallucinations in Large Language Models
by: Ji, Ziwei, et al.
Published: (2024)
by: Ji, Ziwei, et al.
Published: (2024)
Impact of Fine-Tuning Methods on Memorization in Large Language Models
by: Hou, Jie, et al.
Published: (2025)
by: Hou, Jie, et al.
Published: (2025)
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
by: Xu, Benfeng, et al.
Published: (2023)
by: Xu, Benfeng, et al.
Published: (2023)
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
by: Wu, Huiwen, et al.
Published: (2024)
by: Wu, Huiwen, et al.
Published: (2024)
Confabulation: The Surprising Value of Large Language Model Hallucinations
by: Sui, Peiqi, et al.
Published: (2024)
by: Sui, Peiqi, et al.
Published: (2024)
HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
by: Chen, Boshui, et al.
Published: (2026)
by: Chen, Boshui, et al.
Published: (2026)
Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models
by: Liu, Qiang, et al.
Published: (2025)
by: Liu, Qiang, et al.
Published: (2025)
Beyond Fine-Tuning: Effective Strategies for Mitigating Hallucinations in Large Language Models for Data Analytics
by: Rumiantsau, Mikhail, et al.
Published: (2024)
by: Rumiantsau, Mikhail, et al.
Published: (2024)
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
by: WU, Jiageng, et al.
Published: (2024)
by: WU, Jiageng, et al.
Published: (2024)
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
by: Feng, Jiarui, et al.
Published: (2025)
by: Feng, Jiarui, et al.
Published: (2025)
Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)
by: Li, Yuangang, et al.
Published: (2025)
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages
by: chi, Yongdong, et al.
Published: (2025)
by: chi, Yongdong, et al.
Published: (2025)
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
by: Chen, Yuyan, et al.
Published: (2024)
by: Chen, Yuyan, et al.
Published: (2024)
Unifying Large Language Models and Knowledge Graphs: A Roadmap
by: Pan, Shirui, et al.
Published: (2023)
by: Pan, Shirui, et al.
Published: (2023)
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?
by: Hu, Xu, et al.
Published: (2026)
by: Hu, Xu, et al.
Published: (2026)
Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
by: Nguyen, Vy, et al.
Published: (2025)
by: Nguyen, Vy, et al.
Published: (2025)
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
by: Jia, Ao, et al.
Published: (2025)
by: Jia, Ao, et al.
Published: (2025)
Similar Items
-
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs
by: Chen, Hanzhu, et al.
Published: (2024) -
KALE: Enhancing Knowledge Manipulation in Large Language Models via Knowledge-aware Learning
by: Lv, Qitan, et al.
Published: (2026) -
Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models
by: Feng, Yujie, et al.
Published: (2026) -
Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
by: Chen, Xinxi, et al.
Published: (2024) -
Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
by: Chen, Yushuo, et al.
Published: (2024)