Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Maekawa, Seiji, Iso, Hayate, Gurajada, Sairam, Bhutani, Nikita |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates
by: Zhang, Haopeng, et al.
Published: (2023)
by: Zhang, Haopeng, et al.
Published: (2023)
The Rarity Blind Spot: A Framework for Evaluating Statistical Reasoning in LLMs
by: Maekawa, Seiji, et al.
Published: (2025)
by: Maekawa, Seiji, et al.
Published: (2025)
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
by: Maekawa, Seiji, et al.
Published: (2024)
by: Maekawa, Seiji, et al.
Published: (2024)
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
by: Belem, Catarina G., et al.
Published: (2024)
by: Belem, Catarina G., et al.
Published: (2024)
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
by: Iso, Hayate, et al.
Published: (2025)
by: Iso, Hayate, et al.
Published: (2025)
Same Content, Different Representations: A Controlled Study for Table QA
by: Zhang, Yue, et al.
Published: (2025)
by: Zhang, Yue, et al.
Published: (2025)
Less is More for Long Document Summary Evaluation by LLMs
by: Wu, Yunshu, et al.
Published: (2023)
by: Wu, Yunshu, et al.
Published: (2023)
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$
by: Taguchi, Chihiro, et al.
Published: (2025)
by: Taguchi, Chihiro, et al.
Published: (2025)
AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation
by: Iso, Hayate
Published: (2022)
by: Iso, Hayate
Published: (2022)
AmbigNLG: Addressing Task Ambiguity in Instruction for NLG
by: Niwa, Ayana, et al.
Published: (2024)
by: Niwa, Ayana, et al.
Published: (2024)
Effectiveness of Prompt Optimization in NL2SQL Systems
by: Gurajada, Sairam, et al.
Published: (2025)
by: Gurajada, Sairam, et al.
Published: (2025)
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
by: Iso, Hayate, et al.
Published: (2022)
by: Iso, Hayate, et al.
Published: (2022)
Align then Train: Efficient Retrieval Adapter Learning
by: Maekawa, Seiji, et al.
Published: (2026)
by: Maekawa, Seiji, et al.
Published: (2026)
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
by: Kandogan, Eser, et al.
Published: (2025)
by: Kandogan, Eser, et al.
Published: (2025)
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation
by: Ni, Shiyu, et al.
Published: (2024)
by: Ni, Shiyu, et al.
Published: (2024)
Natural Language Processing for Human Resources: A Survey
by: Otani, Naoki, et al.
Published: (2024)
by: Otani, Naoki, et al.
Published: (2024)
When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs
by: Zhao, Beidi, et al.
Published: (2026)
by: Zhao, Beidi, et al.
Published: (2026)
How Does Knowledge Selection Help Retrieval Augmented Generation?
by: Li, Xiangci, et al.
Published: (2024)
by: Li, Xiangci, et al.
Published: (2024)
One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models
by: Zhu, Yutao, et al.
Published: (2024)
by: Zhu, Yutao, et al.
Published: (2024)
Hierarchical Embedding Fusion for Retrieval-Augmented Code Generation
by: Sorokin, Nikita, et al.
Published: (2026)
by: Sorokin, Nikita, et al.
Published: (2026)
On Retrieval Augmentation and the Limitations of Language Model Training
by: Chiang, Ting-Rui, et al.
Published: (2023)
by: Chiang, Ting-Rui, et al.
Published: (2023)
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
by: Li, Mingda, et al.
Published: (2024)
by: Li, Mingda, et al.
Published: (2024)
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models
by: Park, Seong-Il, et al.
Published: (2024)
by: Park, Seong-Il, et al.
Published: (2024)
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
by: Zhang, Taolin, et al.
Published: (2024)
by: Zhang, Taolin, et al.
Published: (2024)
Context Length Alone Hurts LLM Performance Despite Perfect Retrieval
by: Du, Yufeng, et al.
Published: (2025)
by: Du, Yufeng, et al.
Published: (2025)
Metacognitive Retrieval-Augmented Large Language Models
by: Zhou, Yujia, et al.
Published: (2024)
by: Zhou, Yujia, et al.
Published: (2024)
Building A Coding Assistant via the Retrieval-Augmented Language Model
by: Li, Xinze, et al.
Published: (2024)
by: Li, Xinze, et al.
Published: (2024)
BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
by: Li, Mingchen, et al.
Published: (2024)
by: Li, Mingchen, et al.
Published: (2024)
A Survey on Retrieval And Structuring Augmented Generation with Large Language Models
by: Jiang, Pengcheng, et al.
Published: (2025)
by: Jiang, Pengcheng, et al.
Published: (2025)
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
by: Tran, Hieu, et al.
Published: (2024)
by: Tran, Hieu, et al.
Published: (2024)
Entropy-Based Decoding for Retrieval-Augmented Large Language Models
by: Qiu, Zexuan, et al.
Published: (2024)
by: Qiu, Zexuan, 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)
by: Xu, Yilong, et al.
Published: (2025)
Retrieval-Augmented Generation for Electrocardiogram-Language Models
by: Song, Xiaoyu, et al.
Published: (2025)
by: Song, Xiaoyu, et al.
Published: (2025)
Unified Active Retrieval for Retrieval Augmented Generation
by: Cheng, Qinyuan, et al.
Published: (2024)
by: Cheng, Qinyuan, et al.
Published: (2024)
OmniTQA: A Cost-Aware System for Hybrid Query Processing over Semi-Structured Data
by: Shahbazi, Nima, et al.
Published: (2026)
by: Shahbazi, Nima, et al.
Published: (2026)
Retrieval-Augmented Generation for Large Language Models: A Survey
by: Gao, Yunfan, et al.
Published: (2023)
by: Gao, Yunfan, et al.
Published: (2023)
Improving Retrieval Augmented Language Model with Self-Reasoning
by: Xia, Yuan, et al.
Published: (2024)
by: Xia, Yuan, et al.
Published: (2024)
RARE: Retrieval-Augmented Reasoning Modeling
by: Wang, Zhengren, et al.
Published: (2025)
by: Wang, Zhengren, et al.
Published: (2025)
Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems
by: Lumer, Elias, et al.
Published: (2025)
by: Lumer, Elias, et al.
Published: (2025)
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
by: Niu, Cheng, et al.
Published: (2023)
by: Niu, Cheng, et al.
Published: (2023)
Similar Items
-
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates
by: Zhang, Haopeng, et al.
Published: (2023) -
The Rarity Blind Spot: A Framework for Evaluating Statistical Reasoning in LLMs
by: Maekawa, Seiji, et al.
Published: (2025) -
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
by: Maekawa, Seiji, et al.
Published: (2024) -
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
by: Belem, Catarina G., et al.
Published: (2024) -
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
by: Iso, Hayate, et al.
Published: (2025)