arXiv AI August 11, 2026 By Karan Singh, Michael Yu, Varun Gangal, Zhuofu Tao, Sachin Kumar, Emmy Liu, Steven Y. Feng

To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval

arXiv:2604. 00715v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensive situations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 24

SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

arXiv:2509.20377v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks...

By Tomoaki Isoda

llms rag nlp reinforcement-learning benchmarks safety

arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag nlp benchmarks

arXiv AI

Aug 21

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

arXiv:2608. 20281v1 Announce Type: cross Abstract: Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inference time.

By Qian Kou, Xiaofeng Shi, Xiaosong Qiu, Hua Zhou

llms nlp fine-tuning benchmarks safety

Hugging Face Trending Papers

Jul 28

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.

llms rag nlp benchmarks

arXiv AI

Jun 30

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

arXiv:2606. 29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language.

By Heshan Fernando, Quan Xiao, Yan Xin, Tianyi Chen

rag nlp fine-tuning benchmarks

arXiv AI

3d ago

Frozen Memory Is Not Enough: Rethinking External Memory as Extraction

arXiv:2608.17050v3 Announce Type: replace-cross Abstract: Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access...

By Mingyuan Li, Guangsheng Yu, Xu Wang, Shaoxiong Ji

llms nlp safety

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SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

Frozen Memory Is Not Enough: Rethinking External Memory as Extraction