cs.CLMar 2, 2026
SaveURAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
Organizations: Uppsala University · Virginia Tech · The Pennsylvania State University · FPT Software, AI Center · University of Science, VNU-HCM · VNU University of Engineering and Technology · Indiana University · University of Illinois at Chicago
Abstract
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluations concentrate primarily on correctness, which may not fully capture the impact of retrieval on LLM uncertainty and reliability. To bridge this gap, we introduce URAG, a comprehensive benchmark designed to assess the uncertainty of RAG systems across various fields like healthcare, programming, science, math, and general text. By reformulating open-ended generation tasks into multiple-choice question answering, URAG allows for principled uncertainty quantification via conformal prediction. We apply the evaluation pipeline to 8 standard RAG methods, measuring their performance through both accuracy and prediction-set sizes based on LAC and APS metrics. Our analysis shows that (1) accuracy gains often coincide with reduced uncertainty, but this relationship breaks under retrieval noise; (2) simple modular RAG methods tend to offer better accuracy-uncertainty trade-offs than more complex reasoning pipelines; and (3) no single RAG approach is universally reliable across domains. We further show that (4) retrieval depth, parametric knowledge dependence, and exposure to confidence cues can amplify confident errors and hallucinations. Ultimately, URAG establishes a systematic benchmark for analyzing and enhancing the trustworthiness of retrieval-augmented systems. Our code is available on GitHub.
Figures & tables
Figure 1: Illustration of uncertainty in RAG. When asked “Can pregnant women drink coffee?” , the retriever surfaces multiple documents, one stating that “coffee is safe in pregnancy” and another warning that “too much coffee can be risky.” The LLM, however, places disproportionate attention on the first document and produces an overconfident yet incomplete answer , overlooking contradictory evidence. This example highlights how noisy or one-sided retrieval can amplify model overconfidence, leading to potentially harmful or misleading responses.
Figure 2: Illustration of dataset construction and evaluation pipeline for URAG . The upper flow shows how we construct the benchmark, which involves retrieving documents from the database and prompting LLMs to generate a supporting document and wrong answers. After that, we use an NLI model to confirm the difficulty before having annotators check the final result. During the evaluation process, RAG systems decide between a list of wrong answers and the correct answer before measuring performance and uncertainty.
| Dataset | Naive | Iter. 1 | Iter. 2 | Iter. 3 |
| Prompt F | Prompt F | Prompt F | Prompt F | |
| LCA | ||||
| CRAG | ||||
| NewsSum |
Table 1: Progressive improvement in valid distractor generation using iterative NLI-based filtering. Starting from a naive prompt, each iteration applies Prompt F to regenerate distractors that fail the entailment-based difficulty check.
Figure 3: Accuracy and uncertainty difference under self-aware prompting. When the LLM is exposed to its confidence scores, RAT exhibits the strongest degradation in accuracy and stability, while most other RAG methods show reduced uncertainty, particularly on math (Olympiad) and general-domain tasks (CRAG).
Figure 4: Accuracy and uncertainty difference under wrong-aware prompting. Misleading confidence cues cause a consistent drop in accuracy and an increase in uncertainty, indicating a higher rate of hallucinated decisions.
Figure 5: Reverse Correlation between Accuracy and Uncertainty across RAG methods. Each point represents the average accuracy and predictive uncertainty (set size) of one RAG method evaluated on ten datasets. A strong negative correlation ( ) shows that higher accuracy aligns with lower predictive uncertainty across RAG methods.
Figure 6: Impact of Irrelevant Contexts on Accuracy and Uncertainty. We add irrelevant information along with retrieved information on DialFact, Odex, and LCA. Bars show absolute differences between with/without noisy information.
Figure 7: Comparison of RAG accuracy and uncertainty on LLM-Correct and LLM-Incorrect cases. “LLM-Only Correct” and “LLM-Only Incorrect” in the below chart show the Set Size of LLM (w/o retrieval) in two sets. The charts report mean values on the HealthVer benchmark. The extended results are in Table A6 .
Figure A1: Tasks covered by URAG. URAG includes tasks spanning 5 domains, general text, research, code, math, and healthcare, for examining the accuracy and uncertainty in daily-use/abstractive-reasoning/high-stakes tasks.
| Dataset | Description | Calibration | Test | Total |
| Commit Message QA | Evaluating RAG system performance in project understanding | 81 | 82 | 163 |
| CRAG † | Evaluating RAG system performance across multiple domains | 1,181 | 1,149 | 2,330 |
| DialFact | Multiple choice QA subset for dialogue fact verification | 1,000 | 1,000 | 2,000 |
| HealthVer | Health claim verification in MCQA format | 665 | 667 | 1,332 |
| Multi-NewsSum | MCQA generated from Multi-News summarization dataset | 475 | 475 | 950 |
| ODEX | Evaluating RAG systems in simple coding tasks | 220 | 219 | 439 |
Table A1: Overview of RAG Benchmark Datasets.
| Category | Full Question with Options | Answer |
| Commit Message QA | Given the context, which commit message best describes the following code changes? A. Improve speed of rebalance script - This removes the call to nodetool ring , which can get unreasonably slow as the amount of data in a BOP cluster increases. It also adds a couple flags that allow the nodetool status call to be skipped if the user is already sure the sanity checks will pass. B. Reduce pressure on memory in stream tests - This change runs the python garbage collector before and after each stream test. The garbage collector is disabled in the CI since it has a significant impact on the duration of the jobs. C. Refactor FFmpegSource - Using 2 queues for video packets and audio packets. Whenever the queues have space, more packets are read from the stream. This work will allow to remove more easily the audio thread in favor of a scheduled call to refill the audio player. D. Support CUDA stream on memory pool - Now, memory pool will have an arena (bins) for each stream to avoid concurrent streams touch the same memory block | D |
| Multinewsum | Which of the following best summarizes the given document? A. Today’s 11 gubernatorial races are primarily focused on local issues, with Democrats poised for significant gains, possibly taking control of a majority of state offices… B. Republicans already control the North Carolina governorship, having wrested it from Democratic control. They also solidified their hold on Utah, North Dakota, and Indiana… C. Today’s gubernatorial elections are proving to be a difficult day for Republicans, who are struggling to hold onto their seats in Utah, North Dakota, and Indiana… D. It’s a race for the governor’s mansion in 11 states today, and the GOP could end the night at the helm of more than two-thirds of the 50 states. The GOP currently controls 29 of the country’s top state offices… | D |
| OlympiadBench | Let be a positive integer and fix distinct points on a circumference. Split these points into pairs and join the points in each pair by an arrow (i.e., an oriented line segment). The resulting configuration is good if no two arrows cross, and there are no arrows and such that is a convex quadrangle oriented clockwise. Determine the number of good configurations. Options: A. B. C. D. | D |
| DialFact | Given the conversation context, evaluate this claim: “Well, it’s a little different. but if you’re looking for something different, you can try gluten-free pasta. it’s wheat-flavored and comes in a variety of textures and shapes. i really like the rice flour pasta” What is the verification status? A. Supports B. Refutes C. Not Enough Information | B |
| Healthver | Can taking medication to lower fever, such as paracetamol (tylenol) and ibuprofen (advil) worsen COVID-19? A. Supported B. Refuted C. Not Enough Information | A |
| Scifact | Is the following scientific claim supported by evidence? Claim: The risk of female prisoners harming themselves is ten times that of male prisoners. A. Supported B. Refuted C. Not Enough Information | A |
Table A2: Dataset Examples with Full Questions and Options . Wrong DialFact and Wrong Odex share similar question-answer, but we use a different database.
| Olympiad | LCA | CRAG | ODEX | ||||||
| Acc | SS | Acc | SS | Acc | SS | Acc | SS | ||
| FiD | 10 | 0.30 | 3.98 | 0.21 | 4.76 | 0.31 | 3.69 | 0.28 | 3.63 |
| 50 | 0.30 | 3.98 | 0.22 | 4.82 | 0.31 | 3.70 | 0.26 | 3.78 | |
| 100 | 0.30 | 4.00 | 0.22 | 4.79 | 0.31 | 3.69 | 0.26 | 3.76 | |
| 500 | 0.28 | 3.99 | 0.23 | 4.79 | 0.31 | 3.70 | 0.26 | 3.75 | |
| Fusion | 10 | 0.37 | 3.36 | 0.82 | 2.19 | 0.66 | 2.38 | 0.86 | 1.73 |
Table A3: Effect of retrieval depth on accuracy and uncertainty (SS) across representative datasets from different domains. Bold value indicates the best result on the benchmark for each RAG method.
| Dataset file | Samples | Total docs | Avg docs/sample | Total text chars | Avg chars/doc | Total words | Avg words/doc |
| OlympiadBench | 661 | 20,672 | N/A (shared) | 51,167,805 | 2,475.22 | 8,910,741 | 431.05 |
| LCA | 163 | 163 | 1.00 | 42,177,274 | 258,756.28 | 3,879,366 | 23,799.79 |
| CRAG | 2,330 | 11,650 | 5.00 | 4,208,072,919 | 361,207.98 | 179,542,626 | 15,411.38 |
| DialFact | 2,000 | 2,435 | 1.22 | 11,835,703 | 4,860.66 | 1,877,746 | 771.15 |
| Healthver | 1,332 | 4,191 | 3.15 | 88,159,278 | 21,035.38 | 13,167,923 | 3,141.95 |
| NewsSum | 950 | 950 | 1.00 | 6,109,017 | 6,430.54 | 993,322 | 1,045.60 |
Table A9: Statistics of datasets used in the benchmark.