arXiv AI
SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation
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arXiv Computation and Language
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ConfRAG: Confidence-Guided Retrieval-Augmenting Generation
ConfRAG introduces a confidence-guided approach to reduce hallucinations in large language models and selectively trigger Retrieval-Augmented Generation (RAG) only when the model is uncertain. The ConfQA fine‑tuning strategy trains the model to answer correctly or respond with "I am unsure," achieving a drop in hallucination rates from 20‑40% to below 5% across factuality benchmarks. Building on ConfQA, ConfRAG limits external retrievals by more than 30% while maintaining over 95% accuracy in ideal scenarios.
arXiv AI
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AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
arXiv:2606. 29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty.
Hugging Face Trending Papers
Jun 27
AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.
arXiv AI
1d ago
RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation
arXiv:2610.11183v1 Announce Type: cross Abstract: Following retrieved evidence does not guarantee factual correctness: misleading evidence can induce a model to replace an answer it previously gave c...
arXiv AI
5d ago
Evaluating the Retrieval Robustness of Large Language Models
The paper evaluates how robust large language models (LLMs) are when using retrieval‑augmented generation (RAG) in practical settings. It investigates whether RAG always outperforms non‑RAG approaches, whether adding more retrieved documents helps, and whether the order of documents matters, using a benchmark of 1,891 samples across five datasets and three task categories. Experiments with 11 LLMs show generally high retrieval robustness, but performance varies by task and prompting strategy, indicating that adopting RAG should be considered on a case‑by‑case basis.
arXiv Machine Learning
Oct 3
Retrieval from Within: An Intrinsic Capability of Attention-Based Models
The paper introduces INTRA, an attention-based encoder-decoder framework that retrieves directly from its own internal representations instead of using an external retriever. By having decoder attention query pre-encoded evidence chunks, INTRA unifies retrieval and generation, eliminating the typical mismatch seen in retrieval-augmented generation pipelines. Experiments on question-answering benchmarks show that INTRA outperforms strong engineered retrieval pipelines in both evidence recall and overall answer quality.