Cover Image

LLM Enhancer: Merged Approach using Vector Embedding for Reducing Large Language Model Hallucinations with External Knowledge

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rayhan, Naheed, Ashrafuzzaman, Md.
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource Language Using Cross-Lingual Distillation Techniques
by: Kabir, Muhammad Rafsan, et al.
Published: (2024)

Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
by: Lee, Kunil, et al.
Published: (2026)

Dataless Knowledge Fusion by Merging Weights of Language Models
by: Jin, Xisen, et al.
Published: (2022)

Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
by: Sun, Shengyin, et al.
Published: (2023)

Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification
by: Sadi, Abu Adnan, et al.
Published: (2024)

Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
by: Nguyen, Hieu, et al.
Published: (2025)

Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey
by: Agrawal, Garima, et al.
Published: (2023)

Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
by: Zhang, Hongxiang, et al.
Published: (2025)

Model Merging in Pre-training of Large Language Models
by: Li, Yunshui, et al.
Published: (2025)

Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
by: Rahman, Subhey Sadi, et al.
Published: (2025)

Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings
by: Sharma, Kartik, et al.
Published: (2025)

Function Vectors in Large Language Models
by: Todd, Eric, et al.
Published: (2023)

On Large Language Models' Hallucination with Regard to Known Facts
by: Jiang, Che, et al.
Published: (2024)

A Systematic Study of In-the-Wild Model Merging for Large Language Models
by: Hitit, Oğuz Kağan, et al.
Published: (2025)

DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
by: Verma, Neha, et al.
Published: (2025)

Arcee's MergeKit: A Toolkit for Merging Large Language Models
by: Goddard, Charles, et al.
Published: (2024)

Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
by: Shu, Dong, et al.
Published: (2024)

GeoLLM: Extracting Geospatial Knowledge from Large Language Models
by: Manvi, Rohin, et al.
Published: (2023)

Solving Situation Puzzles with Large Language Model and External Reformulation
by: Li, Kun, et al.
Published: (2025)

Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
by: Morrison, Jacob, et al.
Published: (2024)

Adaptive Task Vectors for Large Language Models
by: Kang, Joonseong, et al.
Published: (2025)

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
by: Wang, Shengyuan, et al.
Published: (2025)

Leveraging Graph Structures to Detect Hallucinations in Large Language Models
by: Nonkes, Noa, et al.
Published: (2024)

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
by: Shenaj, Donald, et al.
Published: (2025)

Model Merging for Knowledge Editing
by: Fu, Zichuan, et al.
Published: (2025)

EmbedLLM: Learning Compact Representations of Large Language Models
by: Zhuang, Richard, et al.
Published: (2024)

Measuring and Reducing LLM Hallucination without Gold-Standard Answers
by: Wei, Jiaheng, et al.
Published: (2024)

Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge
by: Lin, Yuanze, et al.
Published: (2024)

Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
by: Binkowski, Jakub, et al.
Published: (2026)

Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
by: Tang, Zilu, et al.
Published: (2025)

Demystifying Embedding Spaces using Large Language Models
by: Tennenholtz, Guy, et al.
Published: (2023)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability
by: Hron, Jiri, et al.
Published: (2024)

Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
by: Han, Zongbo, et al.
Published: (2024)

Objective Metrics for Evaluating Large Language Models Using External Data Sources
by: Du, Haoze, et al.
Published: (2025)

Improving LLM's Attachment to External Knowledge In Dialogue Generation Tasks Through Entity Anonymization
by: Sheikhi, Hadi, et al.
Published: (2025)

Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
by: Yang, Jinluan, et al.
Published: (2025)

EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
by: Zhao, Xuyang, et al.
Published: (2024)

Pre-training Limited Memory Language Models with Internal and External Knowledge
by: Zhao, Linxi, et al.
Published: (2025)

Hallucination is Inevitable: An Innate Limitation of Large Language Models
by: Xu, Ziwei, et al.
Published: (2024)

EvoMerge: Neuroevolution for Large Language Models
by: Jiang, Yushu
Published: (2024)