# Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)

arXiv: https://arxiv.org/abs/2607.04223 · HTML: https://mlsanity.com/paper/2607.04223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 27 of its 29 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Placed in

- Retrieval-augmented generation / [Hallucination detection in RAG](https://mlsanity.com/rag/hallucination-detection-in-rag/)

## Builds on (most cited first, 27 of 27)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model](https://mlsanity.com/paper/2502.02737/) (2025)
- [INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection](https://mlsanity.com/paper/2402.03744/) (2024)
- [AlignScore: Evaluating Factual Consistency with a Unified Alignment Function](https://mlsanity.com/paper/2305.16739/) (2023)
- [RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://mlsanity.com/paper/2401.00396/) (2023)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://mlsanity.com/paper/2407.07071/) (2024)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2407.11005/) (2024)
- [ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability](https://mlsanity.com/paper/2410.11414/) (2024)
- [ContextCite: Attributing Model Generation to Context](https://mlsanity.com/paper/2409.00729/) (2024)
- [MeetingBank: A Benchmark Dataset for Meeting Summarization](https://mlsanity.com/paper/2305.17529/) (2023)
- [Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation](https://mlsanity.com/paper/2108.13134/) (2021)
- [LettuceDetect: A Hallucination Detection Framework for RAG Applications](https://mlsanity.com/paper/2502.17125/) (2025)
- [AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution](https://mlsanity.com/paper/2411.15102/) (2024)
- [Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model](https://mlsanity.com/paper/2310.11648/) (2023)
- [Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.16415/) (2025)
- [Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs](https://mlsanity.com/paper/2305.12191/) (2023)
- [LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals](https://mlsanity.com/paper/2509.21875/) (2025)
- [ConSens: Assessing context grounding in open-book question answering](https://mlsanity.com/paper/2505.00065/) (2025)

## Built on by (most cited first, 1 of 1)

- [When the Wrong Key Wins: Understanding and Detecting Hallucinations in LLMs](https://mlsanity.com/paper/2609.15106/) (2026)

## Abstract (arXiv)

Retrieval-augmented generation (RAG) assistants summarize records in clinical and legal work, where one unsupported sentence can mislead a reader. The contrast between an output's likelihood with and without its source is an established faithfulness score for whole summaries and answers, but it has not been measured as a detector of the individual unsupported sentence in multi-passage RAG answers, against trained verifiers, or for its cost. We implement it as a training-free detector that re-scores a fixed answer under the full context, no context, and each chunk removed, and returns the chunk whose removal lowers a sentence's likelihood most as a candidate supporting passage. We evaluate it on RAGTruth, TofuEval, and RAGBench with six scorers and against five verifiers, up to a large language model (LLM) judge, on identical inputs under a source-level split. Scoring per sentence ranks unsupported sentences better than the answer-level form of the same signal on all three benchmarks, by 0.033 to 0.071 in the area under the receiver operating characteristic curve (AUC). On RAGTruth the training-free score reaches an AUC of 0.717 to 0.745 across scorers and 0.773 with a classifier, above entailment and attribution baselines and level with per-chunk fact-checkers, at about one forty-seventh of the LLM judge's compute on a 1.5B scorer, while a full-context fact-checker and the judge are more accurate and are not improved by it. The signal is weakest on short-answer question answering, where the scorer can answer from memory.