Buchumschlag

Improving Retrieval Augmented Language Model with Self-Reasoning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Xia, Yuan, Zhou, Jingbo, Shi, Zhenhui, Chen, Jun, Huang, Haifeng
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Computation and Language Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
von: Jeong, Minbyul, et al.
Veröffentlicht: (2024)

Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate
von: Huang, Ziyang, et al.
Veröffentlicht: (2025)

Do Retrieval Augmented Language Models Know When They Don't Know?
von: Zhou, Youchao, et al.
Veröffentlicht: (2025)

SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
von: Huang, Yongfeng, et al.
Veröffentlicht: (2026)

Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning
von: Shi, Yaorui, et al.
Veröffentlicht: (2025)

Improving Context Fidelity via Native Retrieval-Augmented Reasoning
von: Wang, Suyuchen, et al.
Veröffentlicht: (2025)

Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
von: Huang, Chengkai, et al.
Veröffentlicht: (2024)

Process Supervision via Verbal Critique Improves Reasoning in Large Language Models
von: Chen, Hao-Yuan
Veröffentlicht: (2026)

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
von: Li, Feiyang, et al.
Veröffentlicht: (2025)

Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations
von: Ranaldi, Leonardo, et al.
Veröffentlicht: (2024)

Improving Language Model Reasoning with Self-motivated Learning
von: Feng, Yunlong, et al.
Veröffentlicht: (2024)

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
von: Wang, Junjie, et al.
Veröffentlicht: (2024)

MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning
von: Zhan, Zaifu, et al.
Veröffentlicht: (2025)

A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
von: Zhang, Qinggang, et al.
Veröffentlicht: (2025)

Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
von: Li, Mingda, et al.
Veröffentlicht: (2024)

Large Language Models Cannot Self-Correct Reasoning Yet
von: Huang, Jie, et al.
Veröffentlicht: (2023)

TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
von: Sui, Yuan, et al.
Veröffentlicht: (2023)

Self-Discover: Large Language Models Self-Compose Reasoning Structures
von: Zhou, Pei, et al.
Veröffentlicht: (2024)

Large Language Models Can Self-Improve in Long-context Reasoning
von: Li, Siheng, et al.
Veröffentlicht: (2024)

ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning
von: Tang, Xiangru, et al.
Veröffentlicht: (2025)

Retrieval-Augmented Generation for Electrocardiogram-Language Models
von: Song, Xiaoyu, et al.
Veröffentlicht: (2025)

TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
von: Sun, Jiashuo, et al.
Veröffentlicht: (2026)

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

RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
von: Wang, Yu, et al.
Veröffentlicht: (2025)

Large Language Models Are Neurosymbolic Reasoners
von: Fang, Meng, et al.
Veröffentlicht: (2024)

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
von: Tran, Hieu, et al.
Veröffentlicht: (2024)

A Survey on Retrieval-Augmented Text Generation for Large Language Models
von: Huang, Yizheng, et al.
Veröffentlicht: (2024)

Retrieval-Augmented Generation with Hierarchical Knowledge
von: Huang, Haoyu, et al.
Veröffentlicht: (2025)

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
von: Xu, Ran, et al.
Veröffentlicht: (2024)

STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
von: Lee, Kyumin, et al.
Veröffentlicht: (2025)

GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation
von: Xiao, Yilin, et al.
Veröffentlicht: (2025)

Learning to Self-Verify Makes Language Models Better Reasoners
von: Chen, Yuxin, et al.
Veröffentlicht: (2026)

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
von: Huang, Yuqing, et al.
Veröffentlicht: (2025)

Reasoning in Action: MCTS-Driven Knowledge Retrieval for Large Language Models
von: Liu, Shuqi, et al.
Veröffentlicht: (2025)

Improving the Efficiency of Visually Augmented Language Models
von: Ontalvilla, Paula, et al.
Veröffentlicht: (2024)

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
von: Ni, Bo, et al.
Veröffentlicht: (2025)

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
von: Xi, Zhiheng, et al.
Veröffentlicht: (2023)

Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
von: Ma, Shengjie, et al.
Veröffentlicht: (2024)

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
von: Jiang, Jinhao, et al.
Veröffentlicht: (2024)

Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
von: Park, Seong-Il, et al.
Veröffentlicht: (2024)