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#small language model Open access Aug 2026

Retrieval Granularity as Evidence Design in Small-Model RAG Question Answering: A Diagnostic HotpotQA Study

Results align with a diagnostic perspective on chunking: using evidence at a task-appropriate level of granularity can improve grounding, auditability, and answer quality, but the observed patterns should be interpreted within the HotpotQA distractor setting, fixed generator, and tested context budgets.

Weimao Ke, Lixia Yang, Mengyang Xu · 0 citations
Preprint Jul 2026

Testing Retrieval-Augmented Generation Systems with Chunk Coverage

Chunk Coverage (CC), an oracle-independent test adequacy criterion for testing the retrieval component of RAG systems, is introduced and results show that CC captures retrieval diversity relevant to effective testing without requiring test oracles.

Jinhan Kim, Samuele Pasini, Paolo Tonella · 1 citation
Preprint Aug 2026

Search-GRT: Guided Retrieval Training of Search Agents to Optimize for Complex Question Answering

Guided Retrieval Training (GRT) is introduced, a novel method that improves the performance of a search agent by restricting the retrieval process during RL training using ground truth information, and enhances training efficiency by achieving better QA performance with fewer training steps.

Aounon Kumar, Sudipta Paul, Vivek Kulkarni et al. · 0 citations
Open access Aug 2026

An Optimization Framework for Retrieval Augmented Generation in Indonesian Educational Question Answering

A RAG optimization framework for Indonesian-language educational question answering using a Human-Computer Interaction learning corpus as a case study is developed and provides a procedure for selecting retrieval and generation settings for a given corpus.

I. K. R. Arthana, N. Gunantara, Made Sudarma et al. · 0 citations
Book Open access Jul 2026

SCORE-RAG: Self-Correcting Exploration-Exploitation Retrieval for Multi-hop Question Answering

SCORE-RAG reformulates multi-hop RAG as a two-phase adaptive process: exploration for dynamic query understanding, followed by exploitation for precise evidence gathering, which enables adaptive query comprehension, reduces error accumulation via self-verification, and produces interpretable reasoning chains for accurate answer generation.

Shuran Zhou, Rui Ling, Junan Chen et al. · 0 citations
Jul 2026

RAGnRoll: Learning to Iteratively Retrieve and Generate Attributable Answer Snippets

This work proposes RAGnRoll, a language model for attributed answer generation within a multi-round Retrieval-Augmented Generation (RAG) framework that leverages the iterative nature of multi-round RAG to train an LLM to incrementally build answers guided by subqueries.

Hanane Djeddal, Laure Soulier, K. Pinel-Sauvagnat et al. · 0 citations