Reliable Decision‑Making via Calibration‑Oriented Retrieval‑Augmented Generation
Authors: Chaeyun Jang
Deukhwan Cho
Seanie Lee
Hyungi Lee
Juho Lee
Venue: NeurIPS 2025 poster ()
Keywords: Calibration
Uncertainty
LLMs
RAG
Primary area: deep_learning
TL;DR: In this paper, we propose uncertainty calibration method, called CalibRAG, for the RAG guided decision-making scenario.

Abstract: Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of generated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, previous RAG methods focus only on retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose a novel retrieval method called Calibrated Retrieval-Augmented Generation (CalibRAG), which ensures that decisions informed by RAG are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared to other baselines across various datasets.

Reliable Decision-Making via Calibration-Oriented Retrieval-Augmented Generation
Chaeyun Jang
Deukhwan Cho KAIST
KAIST
jey9s118kaist.
srcrro_boomin@kasst.oo
Seanie Lee
Hyungi Lee
KAIST
Kookmin University
Lsnfemily022kaist.acco
1nk2708@kookmin..c.
Juho Lee KAIST
juhol
Abstract
Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead hu- mans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of gener- ated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, previ- RAG methods focus only retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose novel retrieval method called Calibrated that decisions informed by RAG are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared other baselines across various datasets.
Generation (CalibRAG), which ensures
Introduction
Large language models [LLMs; 1, 2, have demonstrated remarkable performance on numerous downstream natural language processing (NLP) tasks, leading to their widespread integration into various decision-making processes [5, 6, 7]. However, even with significant increases in model size and the expansion of training datasets, it remains infeasible for L.MMs to encode all possible result, the outputs produced by L.LMs may not consistently
knowledge within their parameters. be reliable for important human Docision-making processes, potentially overlooking key hidden details. Additionally, LLMs frequently provide inaccurate or mislcading information with a high degree of confidence, a phenomenon referred as hallucination [8, 9]. which can lead humans to make flawed decisions. In addition, Zhou et al. [7] has empirically demonstrated that human users often over-rely LLM outputs during decision-makinn making processes, and this over-reliance tends to increase in proportion to the model's confidence. Here, the model's confidence refers to the expression of how certain the model is when asked how confident it is in its answer. Specifically,
"Co-corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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they have found that for answers with high confidence, users show strong over-reliance regardless of whether the answer is correct or not. These findings highlight that utilizing LLMs without proper calibration of their responses and addressing the frequent occurrence of hallucinations can lead to incorrect decisions in high-stakes tasks such as medical diagnosis and legal reasoning, potentially resulting in severe consequences [10, 11, 12].
Retrieval Augmented Generation (RAG) [13, 14, 15] has emerged as a promising method to address hallucinations, which is one of the two key issues when using LLMs in decision-making [16, 17]. Instead of generating answers directly, RAG retrieves relevant documents from external databases and uses them as an additional context for response generation, This approach supplements the in- formation that LLMs lack, resulting in more accurate and reliable responses. However, the database cannot encompass all information, and the knowledge from world is continuously being updated. In such cases, the retriever may retrieve irrelevant documents, which can distract the LLM and lead to the generation of incorrect answers to the question [18]. Moreover, described in Sec. 2.2, due to the LLM's overconfidence in the retrieved document, they still tend to assign high confidence to its responses even when they are incorrect.
Research on uncertainty calibration has aimed address the issue of overconfident outputs in deep neural networks [19, 20, 21J. In image classification, techniques like temperature scaling have proven effective in improving calibration by adjusting logits [22, 23, 24]. However, calibrating LLMs is more challenging due to their sequential token generation and exponentially growing out- put space [21]. Thus, traditional methods like temperature scaling are less effective for long-fom form generation. To address this, Band et al. [6] proposed calibration method targeting probabilities associated with user decisions in L.L -egeeraee guidance but noted limitations in calibrating prob- abilities in RAG contexts.
To address this issue, we propose the Calibrated Retrieval-Augmenne Generation (CalibRAG) framework. CalibRAG allows an LLM using RAG to not only select relevant information to support user decision-making but also provide confidence levels associated with that information by utilizing a forecasting function, ensuring well-calibrated decisions based on the retrieved documents. Here, the forecasting function is the surrogate model that predicts the probability of whether the user's s decision based on the guidance provided by RAG will be correct. We empirically validate that our CalibRAG significantly improves calibration performance as well as accuracy, compared to other relevant baselines across several datasets. Our contributions can be summarized as follows:
We propose the CalibRAG framework, which enables well-calibrated decision-making based on the guidance provided by RAG.
We construct a new dataset by creating labels that indicate how much decisions made using re- trieved documents correctly answer the questions, essential for training the forecasting function.
We outperform existing uncertainty calibration baselines across various tasks involving RAG con- text in decision-making scenarios.
2 Preliminaries
2.1 Decision Calibration of Long Form Generation
As discussed in Sec. since human decision-maker makers tend to over-rely on the outputs of LLMs during the decision-making process, it is crucial to ensure that the confidence in LLMs' outputs is well-calibrated. To address this problem, Band et al. [6] proposes decision calibration, which aims to align the confidence of the model's predicted output with the accuracy of the user's decision based on the model output. This allows the user to make a reliable decision based on the model's 's confidence. Thus, to achieve this goal, we need to ensure that the model not only generates factual information but also its confidence in the generated responses accurately reflects the likelihood of correctness.
To formalize the problem, we introduce the following notations. Let z E X represent the question or task for which a user needs to make decision (e.g., "Should take melatonin to help with jet lag after a long flight?"). and let E y denote the corresponding true answer (e.g., "Yes, if taken at a local bedtime."). Here, X' and ) are the set of all possible questions and answers, respectively. Given the question æ, the user provides an open-ended query (1x) (e.g., "Write a paragraph about the effects of melatonin on jet lag.") to an L.LM as a prompt to gather information for the decision making about x. The LLM, denoted as M, generates a long-form response to the query, i.e., z ~

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Contriaver
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Base+RAG Model
Cumulative Accuracy
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(a)
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Figure I: (a) Cumulative accuracy with top-K documents on our synthetic validation set (see Sec. 3.3). cont eever-mmma
gains 1% compared to top-1 when the top-9 documents are used, showing that the top-1 hit is often not optimal. CalibRAG reaches higher top-1 accuracy and gains little from additional documents. (b, e) Reliability diagrams NaturalQA. For -1ama-3. 1-82 trained under the Number baseline (see Sec. 4), adding retrieved document (c) raises accuracy relative to no-document baseline (b) but also increases ECE, indicating greater over-confidence. Bar height is the mean accuracy in each confidence bin; darker shading marks bins with more predictions.
M(z]g(z)), which serves as the guidance for the decision-making process. For the sake of notational simplicity, unless specified otherwise, we will use q place of ((x). Given the question æ and the generated response the user leverages a forecasting function f X' x Z + Apy to assess all possible answers y E D. where Apy denotes simplex over the set y and is the space of all possible responses from M. The goal is to use the forecasting function f to ensure that, given the long-fom generated LLM response 2, the user makes calibrated decisions on the question- question-answer answer pairs (x, y). Based on this, Band et al. [6] introduces formal definitions for three types of calibrations with varying conditions. For instance, the LLM is confidence calibrated [25] with respect to the forecasting function f if is calibrated on the joint distribution p(r, y. 2), that is, for any / E [0,
Pr = arg max f(, 2) jED'
max f(z, 2), 3) B,
JE(y
where (xx z); denotes the element of vector (1),2)
However, the method proposed by Band et al. [6] to tackle this calibration problem has three major limitations. 1) It requires supervised fine-tuning for three different LLMs, including the LLM re- sponsible for generating a response z and the forecasting function f parameterized with two LLMs. 2) it further needs proximal policy optimization [PPO; 26] for fine-tuning the LLM for response generation, which known to suffer from training instability [27]. 3) It cannot be directly applied to calibrate the probabilities associated with the user decisions based on the guidance by RAG.
2.2 Retrieval Augmented Generation (RAG)
RAG [13] employs Dense Passage Retrieval [DPR; 28] to retrieve relevant documents for question answering. DPR encodes questions and documents independently, enabling precomputation and in- dexing document embeddings. At inference time, only the question embedded and matched against the indexed documents via similarity search. Retrieved documents are then provided as ad- ditional context to an LLM, often improving accuracy of answer. Despite its effectiveness, RAG remains vuinerable to retrieval errors. Since retrievers typically trained in unsupervised man- ner [29,30 30], their similarity scores do not necessarily reflect the utility of documents for downstream Decision-making As shown in Fig. Ia, the top-ranked document retrieved by Contriever [29] often leads to incorrect predictions, whereas lower-ranked documents may yield better outcomes. Fur- thermore, incorporating irrelevant documents can mislead the LLM, resulting overconfident but incorrect answers, as illustrated in Fig. 1c. As an alternative to improve retrieval quality, reranking methods [31, 32] have been proposed to reorder candidates based on relevance signals. However, these approaches are typically optimized for ranking metrics (e.g.. MRR, NDCG) rather than the correctness of downstream decisions, and thus do not produce calibrated confidence estimates. We provide detailed discussion of why reranking methods fail to support decision calibration in Sec. A. Existing RAG methods, including those incorporating reranking, lack mechanisms to assess the con- fidence of retrieved documents. Addressing this limitation requires not only identifying documents
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Should I melatonin help with jet lag after a long flight?
4 Write a paragraph about the effects of melatonin on lag. decide quick E2 temperature
User
melatonin reduced symptoms of Desynehronosie when taken at the correct
melatomin natural hormone that regulates the sleep-wake eyele...
relative the new time zone...
Retriever
Reranker
Ours (CalibRAG)
w. uncertainly
Reranker
q
CalbRAG
calibration baseline
Beranker select decument (higher celevance soor))
CallBRAG select 2 document (higher decision confidence) z: Melatonia signlficantly reduces symptoms of jet lag when administered accurding to the new local time. confidence score 84% given that your
2: Melatonin naturally occurring hormone that
plays key rale in regulating sleep patterns.
User: is
Yes (cunfidence: 9/10]
I'm not sure what to do. Is this suggesting should take melatonin
y: should consider taking melstonin, but only the correct time based the zone.
Figure 2: Comparison between CalibRAG and other reranking methods during inference. In contrast conventional methods that rely on relevance scores to rerank retrieved documents, CalibRAG leverages confidence score derived from the user's risk tolerance t to guide the reranking process.
that better support accurate downstream decisions, but also calibrating the likelihood that such deci- sions are correct given the retrieved context.
3 CalibRAG: RAG for Decision Calibration
Overview. When LLMs support user decision-making using RAG, the ability to predict the relia- bility of those decisions in advance can significantly improve the safety and trustworthiness of the overall experience. Given a task x on which a user must make decision, we assume an associated open-ended query q. The retriever then selects a relevant document d from an external database. Conditioned on y and d, the LLM M produces long-form guidance addition to this guidance, we must also provide a confidence score e. This score can be obtained in two ways: (i) by asking M to verbalize its own confidence, or (ii) by predicting it using sep- arately trained forecasting function f. our framework. because verbalized confidences from M often poorly calibrated, we adopt the latter. The forecasting function outputs f(t, q, d), probability that the user's decision will be correct, conditioned on a temperature parameter of the decision-makee making user, g. and d. While prior work defines f(r, 2) over LLM outputs [6]. our for- mulation ftt q, d) predicts correctness before 2 is generated. This enables reranking and simplifies supervision. The user ultimately consults and to make decision, and goal is to ensure that the predicted confidence c is well aligned with the empirical accuracy of decision- decision-making making scenarios. Fig. 2 right illustrates the overall usage process of CalibRAG.
support the decision. In
3.1 Problem setup
To train and evaluate the forecasting function f. we introduce an LLM-based surrogate user model U that can simulate human decision-making without requiring costly human annotations [6]. We construct prompts such that U makes decisions by referencing both the guidance z and the con- fidence score c, deciding whether to accept the guidance based on the confidence (see Sec. for prompt details). Since the output of the surrogate user model U is long-form text rather than class label, we utilize the GPT-4o (ef-4o-mini model (denoted by G) to automatically evaluate whether the user's response matches the correct answer y.
To construct supervision signals for f. we sample responses from U across various decoding tem- peratures t E T to simulate a range of user behaviors. This temperature reflects behavioral traits of the user U, such as risk tolerance or decision urgency. For instance, if the user needs to make a decision quickly and prefers reliability, they can choose a lower I. On the other hand, if the user has more time and is open to exploring diverse alternatives, they may opt for a higher I. For each combination (t, d). we collect R responses and use the proportion of correct ones soft label

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for training f. As result, the label is represented as probability between [0. 1], used as a soft label for binary modeling or extended to a multi-class histogram to support tner-graind calibration.
Then, our final goal is to satisfy the following binary calibration condition:
Pr ly (z, 2, f(t,q,d) f(t, q, d) = || = 3, vB E [0, 1),
which means that the predicted confidence / should match the actual accuracy of user decisions.
As discussed in Sec. and illustrated in Fig. Ic, asking the RAG model provide its own confidence often leads to excessively high confidence scores, which can cause users to overtrust inaccurate information. In this work, to overcome this limitation, we train forecasting function f(t, q, d) instead to satisfy the calibration condition in Eq. 1, using supervision signals derived from self- consistency sampling guided by U. This approach enables better modeling of real user behavior and allows for more effective confidence calibration, ultimately resulting in safer and more trustworthy decision-making system.
3.2 Modeling and Training
To model the forecasting function f. it is essential to have the capacity to sufficiently analyze the impact of the generated guidance on the actual decision. For this reason, we use pre-trained LLM M, responsible for generating 2, as the base feature extractor model fleat For details of the feature-extraction process, please refer Sec. B.4. To incorporate the temperature parameter t, we apply a Fourier positional encoding [33] that maps the scalar t E R to a -N-dimensional vector as follows:
2T
PE( (oss(w))) sinnu)))....,,s(((((t)) sos(u])) Wr 2
(2)
tmax tmin
where N is the number of frequencies in the encoding. The resulting PE(() E R is projected with a learnable matrix W E Ax2N and then added element-wise to the base feature frea d). Additionally, model the probability that U2 z, f(t a linear classification head on top of the extractor Siea derived from the frozen LLM M. For parameter-efficien fine-tuning and avoid abrupt representation shifts, we keep M frozen and train only the LoRA adapters [LoRA; 34] and the lightweight head.
q, ft,, leads a correct decision, we place
The resulting forecasting function is defined as:
(3) where (I)) 1/(1 + exp(-x)) is the sigmoid function. Here, W Whead bread, Wp. and the LoRA parameters WLORA are all learnable. This formulation allows f to condition its prediction on both the semantic compatibility of the q-d pair and the user-specific behavior encoded by t. providing an uncertainty-aware estimate of decision correctness. Then we train f by minimizing the following log-likelihood loss:
f(t, q1 d) = = (Witau heaa (concaCon d]; WLORA) + W, PE(t)) blsad)
C
1 S
Σ (t.g.d.b)E
[blog ftt, q, d) + (1 b)) log(1 f(t, q, d))]
(4)
where is the training dataset and E [0 is the soft label derived from self-consistency sam- pling. This objective encourages the predicted confidence ft,, q, to match the empirical correct- ness probability, enabling the model to generalize across varying decision behaviors encoded by t, while leveraging the LLM's latent representations for calibrated prediction. Although the primary formulation uses soft binary labels, we also investigate a multi-class variant where the correctness distribution is discretized into histogram bins and train f using categorical objective. Full experi- mental results and comparisons are provided in Sec. 4.
Remark. Among various scoring rules [35, 36, 6] used to measure the forecast quality of functions predicting the true probability p. the strictly proper scoring rule has the advantage that its unique maximizer is the true probability p. Consequently, training a forecast function using a strictly proper scoring rule as the training objective ensures that the forecasts are learned to be as close as possible to the true probability as the number of training examples increases. Note that the loss in Equation 4 to train our forecast function f is an example of strietly proper scoring nule, the logarithmic score. This makes our loss function crucial for training to produce well-calibrated predictions.
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3.3 Synthetic Supervision Data Generation
To conduct the supervised learning discussed in Sec. 3.2, it is essential to construct an appropriate
synthetic training dataset S consisting of the triples (4, q, d, b). We first extract the (I, y) decision- making task pairs from the following three Question Answering datasets: 1) TríviaQA [37], 2) SQuAD2.0 [38], and 3) WikiQA [39] datasets. Then, for every x: in the training dataset, we generate model. At this point, it is
an open-ended query based on each I, using the GPT-4o-ming important to note that instead of retrieving only the single top document with the highest similarity score from the retriever model for each query q, we retrieve the top 20 documents. There are two reasons for this. First, as illustrated in Fig. la, a large number of low-ranked documents actually help the surrogate user make a correct decision. If we only include the top-1 documents, many of which would be labeled as incorrect, the synthetic dataset would be highly biased to negative samples. Second, using only one d per (zz, y) pair for labeling and training could result in the model overfitting to the label without learning the relationship between q and d adequately. By pairing the same q with various d's, the model can learn from positive and negative samples, improving its ability to generalize. After retrieving multiple documents, we provide each (q. d) pair to the RAG model M, which generates the guidance based on d.
Then, the user model U receives the task r and guidance z, and samples responses with the decoding temperature f, which reflects behavioral variation during sampling. To estimate the reliability of the generated decision, we sample R = 10 from U at the same temperature and evaluate each using the correctness function G, which compares the decision with the ground truth answer y. The soft label b E [0, 1] is then computed as the proportion of correct responses among the R samples. Thus, for each (Ir, pair, we generate multiple training quadruples (4, q, d, b), each corresponding to different /. setting and document retrieved. Refer to Sec. E for examples of our synthetic data.
3.4 Inference
After finishing the training of the forecasting function f. we perform inference for new decision task x² through the following four stage process:
Stage 1: Initial retrieval of documents. Given an open-ended query q", derived from the orig- inal question x", we begin the document retrieval process using the retrieval model. Similarly to the training data generation process, retrieve the top K relevant documents from the external database, denoted by D == [d;),K]] The goal of this stage is to construct a diverse set of candidate documents that may contain valuable information for producing the correct answer y.
Scoring and selection of documents. Once the K candidate documents are retrieved, we estimate the decision confidence of each document with our trained (-conditioned forecasting function /. At inference time, the user may choose $tş to reflect their decision preference, with lower values for cautious, consistent decisions and higher values for exploratory reasoning. Regardless of the original retricval similarity score, each document is then reranked by its predicted confidence. Concretely, we sort the documents in descending order of the probabilities {f g", (dd) ) which represent the chance that the user will reach a correct decision guidance is generated from each document. The top-ranked document is selected for the next stage. Here, if the predicted probability for the highest-ranked document d" falls below a predefined threshold c (defaulted to 0.5, with further details provided in $ D.1), we consider the guidance 2 insufficient for reliable decision at temperature 6. In this case, we move on to Stage 3, where we retrieve a new set of K candidate documents search for documents that provide higher confidence. If this condition is not met, we move forward to Stage 4.
Stage 2:
Stage 3: Reformulating the query (Optional). If the predicted probability for the highest-ranked document d' is lower than a pre-defined threshold in Stage 2, to retrieve a new set of K candidate documents, we reformulate our open-ended query q" into q-* by emphasizing more important con- tent from the question x. This reformulation focuses on extracting key aspects of the original task, ensuring that the next retrieval attempt targets more relevant and helpful documents. After reformu- lating the query, we repeat Stage and Stage 2 once again. Examples of query reformulation are shown in $ C.
Stage Final decision. After retrieving the document d', we generate the guidance z" using the RAG model M. Then, the user model U makes a decision U(*", z", [(q", d' (()), with the

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CT-Probe
Linguistic
Number
CalibRAG
CallbRAG-muitt
0.4
(a) BM25 w/ NQ
(b) BM25 w/ WebQA
(c) Contriever w/ NQ
(d) Contriever w/ WebQA
Figure 3: Evaluation results of the baselines and CalibRAG using two retriever models: BM25 and Con-
triever on NQ and WebQA. We report four Parrics---URRR 1-ACC, ECF, and where lower values indicate better performance.
decoding temperature t selected in Stage 2. This decision is compared with the correct answer y by determine its accuracy.
Experiments
Implementation detail. For all experiments, following Sec. 3.3, collect total of 20,870 sam- ples for training and 4,125 for validation. All evaluations are conducted in a zero-shot setting on held-out tasks that disjoint from both the training and validation sets. Unless otherwise speci- fied, we use 1.1 ama-3. 3. 1- [3] as both the RAG model M and decision model U. However, we also conduct ablation studies diverse model structures, and the results are presented in Sec. To evaluate long-form generated answers, we employ GPT-44 -4o-mini as the evaluation model G.
Baselines. We compare CalibRAG with the following relevant baselines.
Uncertainty calibration baselines: (1) Calibration Tuning [21] methods employ a model that
outputs probabilities for answering "Yes" "No" the question, "Is the proposed answer true?" These probabilities allow us to measure the uncertainty calibration of the response. As baselines, we consider two variants: CT-probe, which adds classifier head to estimate the probability of correctness, and CT-LoRA, which utilizes the normalized token probability between the tokens "Yes" and "No." " (2) Verbalized Confidence Fine-tuning [40, 41, utilizes verbalized tokens to represent the model's confidence. In this case, we also consider two baseline variants: Number- LoRA, which expresses confidence
integer between and 10, and Linguistic-LoRk which uses linguistic terms (c.g.. "Doubtful" or "Likely") to indicate confidence. For all uncertainty cal- ibration basclines, guidance and confidence are generated based the top-1 document retrieved by the retriever.
Reranking and Robust RAG baselines: Although CalibRAG primarily designed to enable well-calibrated decision-making in RAG-guided scenarios, it can also be interpreted rerank- ing approach for retrieved documents in downstream tasks as a consequence of Stage 2 during inference. Accordingly,
compare CalibRAG against reranking baselines and one robust RAG baseline: (1) Cross-encoder with MiniLM--6-v22, which reranks documents based on the similarity score of the jointly embedded query and documents with cross attention.
(2) LLM- rerank [42], which prompts the LLM, GPT-3. 3. 5-tubbo to rerank documents by leveraging the relationship between the query q and the document d. (3) SelfRAG [43], robust RAG base- line that dynamically determines the necessity of retrieval and self-evaluates the relevance of retrieved document d to the query q, as well as the usefulness of the generated guidance for q, using special tokens such as "Retrieve", "IsREL", "IsSup". and "ISUSE".
CalibRAG. Unless otherwise specified, CalibRAG reranks the top-20 documents at inference. The forecasting function evaluated by marginalizing over a set of six decoding temperatures te (1.0, 1.1, ... 1.5). This is because, in the absence of explicit information about user's prefer- ence, the forecasting function cannot accurately model the user's behavior under a single decoding temperature. To reflect this variation, we approximate marginalization by averaging over these six values, as exact integration over all possible is infeasible. Building on this, CalibRAG-mulii ex- tends AG multi-class setting by modeling the correctness histogram across bins (0-10).
Evaluation metrics. We evaluate all the models terms of accuracy, AUROC, and various cali- bration metrics such as Expected Calibration Error [ECE; 44], Brier Score [BS; 45). For clarity and
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CT-Probe
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0.6
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85
(a) MedCPT w/ BioASQ- -
(b) MedCPT w/ MMLU-Med
(c) MedCPT w/ PubMedQA
Figure 4: Evaluation results of the baselines and CalibRAG utilizing MedCPT BioASQ-Y/N, MMLU- Med, and PA. report four metrics 1-AUROC, 1-ACC, ECE, and Brier Score where lower values indicate better performance.
Table I: Comparison of RAG variants across datasets.
(a) Comparison reranking RAG methods. Baseline reranking scores are treated as confidence.
(b) Comparison of robust RAG methods using Llama-24 2-7B as M.
Data
Method
AUROC(()
ACC(T)
Data
Method
AUROC
ACC
ECE
BS
HotpoiQA
Cross-cmonder
LLMarerank rerank
60.57
38.52
0.248
02997
NQ
SelfRAG CalibRAGi
63.5
48.4
37.4
36.2
0.522
0.258
0.545
0.287
CalibAGG
72.47
42.37
0.106
0.206
WebQA
CalibRAG
SelfAG
68.8
40.5
39.7
0.217
0.262
consistency, we adopt the 1-AUROC and 1-ACC notation in plots so that all metrics can be inter- preted under the convention that lower values indicate better performance. Details regarding these metrics can be found in Sec. B.3.1.
4.1 Main Results
Comparison with uncertainty calibartion baselines. Fig. and Fig. present a comparison of uncertainty-based baselines in both general and specific domain tasks.
For the general domain, we evaluated CalibRAG on the Natural QA (NQ) [46] and WebQA [47] datasets. To demonstrate that our method performs well across different retrievers, we conducted experiments using both Contriever [29] and BM25 [48] retrievers. The results in Fig. clearly show that CalibRAG and CalibRAG-mult
outperform other baselines in all metrics. In particu-
lar, significantly reduces calibration metrics across all datasets and retriever settings, indicating that CalibRAG effectively calibrates the decision peciiinn-making process compared to other baselines. Ad- ditionally, while CalibRAG is not explicitly designed improve accuracy, it naturally identifies documents that are more likely to lead to correct decisions. As result, it also enhances accuracy compared to other baselines. We also evaluate CalibRAG without reranking, which still shows im- proved calibration metrics compared to both the baselines and the setting where baseline confidence scores are used for reranking. Detailed results can be found in Sec. D.
For domain specific evaluation, we assess CalibRAG on the BioASQ-Y/N, MMLU-Med, and Pub- MedQA datasets from the MIRAGE benchmark [49], which focus on the medical domain. Since domain-specific retrievers are necessary in this field, we utilize MedCPT [50], retriever trained on user click logs on PubMed corpus. Note that the rest of CalibRAG model. including the LLM M and the forecasting function, which have been trained on TriviaQA, SQuAD2.0, and WikiQA, re- mains fixed. Thus, this setup evaluates robustness of CalibRAG when applied to an unseen retriever and out-of-domait datasets. The results in Fig. 3 clearly demonstrate that CalibRAG outperform other baselines across all metrics, even in specialized domain scenarios. Similar to the general do- main, CalibRAG significantly improves calibration metrics across all datasets, showing its ability to effectively calibrate the decision-men makinn process even with an unseen retriever and dataset.
Comparison with reranking and robust RAG baselines. CalibRAG retrieves the top K doc- uments during inference and selects the one most likely to lead to a correct decision, using it to generate guidance and confidence for well-calibrated decision-making process. To further evalu- ate effectiveness, we compare AG against reranking and robust RAG baselines Table la and Table lb.

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0.30
44
flo.4)
Callbrag
42
ACC (9a)
40
CalbRAG
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44
0.10
42
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1.1
1.2
1.3
1:4
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40
Contriever
Contriever
User model decading temperature
Number Retrievals
WebQA
WeBOA
(a) Ablation conditioning
(b) Number of retrievals
(c) Effect of query reformulation
Figure 5: (a) Calibration with and without temperature conditioning on the NQ dataset using Contriever. (b) Effect of the number of retrieved documents reranking performance the WebQA dataset using BM25. (c)
Impact query reformulation during inference.
First, we conduct a comparison with reranking baselines using the HotpotQA [51] dataset. To as- sess the calibration performance of existing reranking baselines, use their reranking scores as confidence values. Surprisingly. Table la shows that CalibRAG not only improves calibration per- formance compared the basclines but also enhances accuracy. This result suggests that, while our inference procedure is designed to identify documents that lead to correct decisions, it also ef- fectively retrieves documents that are highly relevant the query æ. The confidence assignment method for reranking models and the comparison with the BEIR reranking benchmark can be found Sec. D.2.
Next, we compare CalibRAG against a robust RAG baseline using the NQ and WebQA datasets. To evaluate the calibration performance of SelfRAG, we use the "Utility" tokens the fidence score. Similar to the reranking baseline comparison, Table 1 demonstrates that CalibRAG consistently outperforms other baselines across all datasets and metrics.
4.2 Ablation Studies
Does temperature conditioning improve calibration? To evaluate the impact of temperature- aware modeling, we compare our full model [tt, d) with lemperature-agnostic variant (4 trained without conditioning on user decoding behavior. As shown in Fig. 5a, temperature-awan forecasting significantly improves ECE across user model temperatures. In particular, flg, d) tends to overestimate confidence under higherrtemperature sampling, leading to increased ECE, In con- trast, our proposed ft,, q, d) correctly adapts to user-specific (decoding) behaviors and maintains relatively low ECE across the full temperature range. This confirms that incorporating user variabil- ity via temperature conditioning enables
reliable decision calibration.
How does the number of retrieved passages (K) impact reranking? We K = 20 docu- ments for reranking in all experiments, balancing the computational cost and the performance the decision-making task. To validate our choice, we plot accuracy as function of the number of doc- uments for reranking in Fig. 5b. The results show that performance improves up to 20 documents, but the gains diminish beyond 40 documents, supporting
choice of 20 documents.
Effect of Query Reformulation During inference, if none of the retrieved documents in Stage 2 are predicted to contribute to correct decision, CalibRAG can optionally proceed to Stage 3, where it reformulates the query and retrieves a new of K documents. To evaluate whether this optional step improves performance, we conducted experiments on the NQ and WebQA datasets using both BM25 and Contriever retrievers. As shown Fig. 5c, incorporating Stage 3, denoted as CalibRAGt, consistently improves performance across all cases. However, this improvement comes the cost of additional computation, as reformulating the query and retrieving new documents require extra processing.
5 Conclusion
In this paper, we introduced CalibRAG, simple yet effective framework designed to ensure that the RAG-guided decision-making process is well-calibrated. Our experiments demonstrated that CalibRAG significantly enhances QA performance within the RAG setting across various datasets and retriever models. Moreover, ablation studies showed that CalibR AG effectively aligns model

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confidence with factual correctness, resulting in improved decision decision-makiee making accuracy and calibra- tion. Overall, CalibRAG stood as robust solution for enhancing the reliability of RAG-based L.M guidance in decision-driven scenarios. However, creating synthetic datasets and training the forecasting function for decision calibration may introduce some overhead. Nonetheless, accurately calibrating language model confidence is crucial, making this approach both valid and worthwhile.
Acknowledgement
This work was supported by Institute of Information & communications Technology Planning & (valuation((ITP) grant funded by the Korea goVe) Intelligence Graduate School Program(KAIST). This work was supported by Institute Infor- mation & communications Technology Planning & Evaluation(IITP) grant funded the Korea government(MSIT) (No.RS-202) RS-2022-00509929, Global Al Frontier Lab). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea gOV))) (NRF-2022R ASA708390812)) This work was supported by Institute of Information & commu- nications Technology Planning & (VaI grant funded by the Korea govermmen((MSIT) (No.2022-0-0018%) 88, Development and Study of AI Technologies to Inexpensively Conform to Evolv- ing Policy on Ethics). This work was supported by the Institute of Information & Communications Technology Planning & (ITTP) grant funded by the Korea (DverTmen((MSTT) (No.RS- 2025-02219317, Al Star FellowshhppKookminn University)). This work was supported by Artifi cial intelligence industrial convergence cluster development project funded by the Ministry of Sci- ence and ICT(MSIT, Korea) & Gwangju Metropolitan City. This research was supported by the MSIT(Minnstry of Science, ICT), Korea, under the Global Research Support Program in the Digital Field program(IITP- 1-244R5-2022-004 Communications Technology Planning & Evaluation).
Artificial
supervised by the (ITPttns for Information &
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Answer: [Ys]]
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Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
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A Related Works
A.1 Uncertainty Calibration in Language Models
Traditional calibration methods rely on token-level log probabilities [25], but modern LLMs gen- erate text autoregressively by multiplying conditional probabilities [4]. Estimating semantic-level probabilities would require marginalization over all possible sequences, which computationally intractable. As result, token-level probabilities often fail to provide reliable confidence estimates
for long-form text generation.
Prompt-base based approaches aim to address this problem by cliciting verbalized confidence scores [40, 52]. For example, model can be prompted with: "Express your confidence as number between 0 and 100." If it responds with "90", this value is interpreted as its confidence level. However, LLMs often exhibit overconfidence zero-shot settings, resulting in poorly calibrated outputs [9]. Although RAG can mitigate this issue, when the retrieved context is unreliable, LLM may still demonstrate overconfidence, leading to misleading conclusions. Addressing this challenge remains essential for improving LLM reliability in complex decision-making tasks.
A.2 Methods for Enhancing RAG Robustness
Recent advancements in reranking for RAG have largely focused on enhancing the relevance of retrieved documents with respect to the input query. For example, LLMM-ased rerankers leverage semantic representations reorder documents based on their relevance [42], while cross-encoder- based rerankers jointly encode query-document pairs to model their interaction more precisely [53]. These approaches are highly effective in improving retrieval relevance and downstream QA perfor- mance. However, they are fundamentally designed to rank documents by relevance, not to assess how the retrieved information influences the correctness of the final user decision based on the LLM- generated answer. Thus, the resulting scores, although often normalized between 0 and 1, are not calibrated probabilities of correctness and cannot be directly used for decision calibration.
Similarly, Self-RAG [43] introduces the notion of utility scores for retrieved documents to identify potentially helpful content. While this provides signal for filtering noisy documents, the utility score reflects plausibility rather than empirical correctness. As such, these scores are neither opti- mized for nor aligned with standard calibration metrics such as ECE, NLL., Brier Score.
In contrast, our approach directly addresses this gap by training a forecasting function to output calibrated confidence scores that reflect the actual correctness of decisions made by a surrogate user model. We explicitly supervise the forecasting function using binary labels that indicate whether the model's final prediction is correct, and optimize this function using strictly proper scoring rules. This ensures that the predicted confidence scores match the empirical likelihood of correctness, thus enabling true decision calibration rather than merely relevance estimation.
This fundamental difference supervision signal (relevance vs. correctness) and objective (ranking vs. calibration) delineates the core novelty of our work from prior reranking-based approaches. By aligning the model's confidence estimates with empirical decision accuracy, our method offers a principled and interpretable framework for improving trustworthiness in RAG systems.
B Experimental details
Our implementation builds on key libraries such as PyTorch 2.1.2 [54], Hugging Face Trans- formers 4.45.1 [55], and PEFT 0.7.1,3 providing robust foundation for experimentation. We model, an open-source multilingual LLM available
employ the L.lama-3 3-1--88-Tnsttruee on Hugging Face. Our experiments conducted NVIDIA RTX 3090 and RTX A6000 GPUs. Additionally, we utilize the official facebookresearchh cussearch-contae cont renrrieerr repository and the elastic-researee -b2 repository for our retrieval model. We also use MedMCT
httrp:///ggthuub..cmmm/gggggggg
http://pgg..aaaaaoooo///oe//eee/ ama-333.mmmmmmmmmmmm 88- 1 Instruct
5https://github. http://hthh...ooomococoooeeeeeea 9tttps:/ww.eeastiii
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based on the MedRAG framework.? For training calibration tuning baselines, we reference the calibration-tuning repository."
B.1 Datatsets
Train Datasets SQuAD [56, 38] is a reading comprehension dataset sourced from Wikipedia, containing questions answered by text spans from the articles. WikiQA [39] is question-sentence pair dataset from Wikipedia, designed for open-domain question answering and includes unanswer- able questions for research on answer triggering. TriviaQA [37] a reading comprehension dataset with questions authored by trivia enthusiasts, paired with evidence documents from Wikipedia and other web sources. We randomly sampled 10,000 data points each from TriviaQA and SQuAD2.0, and collected all 873 training samples from WikiQA. In addition, we incorporated non-overlapping samples from SQuAD1.0, resulting in combined training dataset of 61,886 examples after dedu- plication. For the validation set, we gathered 2,000 samples each from TriviaQA and SQuAD, along with 126 samples from WikiQA, and added non-overlapping samples from SQuADI.0, .. yielding total of 12,643 validation data points. All null values were removed prior to finalization. We downloaded all these datasets Hugging Face datasets" %
For the construction of the labeled dataset S used to train the forecasting function of CalibRAG, we sample a temperature
Uniform[1.. 2.0] for each query q and retrieved document d. For each triplet (f, q, d), we perform user decoding 10 times and assign a soft label b indicating the ratio of generated answers that contain the ground truth. The final dataset S thus consists of tuples in the form (t, d, b). The dataset will be made publicly available upon the publication of this work.
Evaluation Datasets For zero-shot evaluation, we employ several datasets covering diverse do- mains and question types. HotporQA [51] is a multi-hop question-answering dataset requir- ing reasoning across multiple supporting documents from Wikipedia to find answers, emphasiz- ing more complex retrieval and reasoning process. WebQA [47] is open-domain question- answering dataset consisting of natural, conversational questions paired with web documents, target- ing real-world, context-rich scenarios. Natural Questions (NQ) [46] is another large-scale question- answering dataset, designed to answer questions based on Wikipedia articles, containing both long- form and short-form answers. These datasets used without additional training, providing bust evaluation of the generalization capabilities of CalibRAG across different domains and question types.
We also evaluate domain-specifer datasets, including BioASQ [57], a biomedical QA dataset con- taining factoid, list, and yes/no questions derived from PubMed articles, as well as Medical Infor- mation Reureeval-Augmenteed Generation Evaluation (MIRAGE) [49] and textbook corpus.
B.2
Hyperparameters
Table 2 outlines the hyperparameters used for training the base model and LoRA, including key parameters such as learning rate, batch size, and LoRA-specific settings like rank and alpha.
B.3 Evaluation metrics
To evaluate long-form text, we utilized gpt 40 110-44ommin mini to compare the ground-truth answers with the predicted answers in all cases. Based this comparison, we labeled each instance as correct or incorrect accordingly.
B.3.1 Calibration metries
Expected Calibration Error [ECE; 44]:
M
Bml
ECE
-
n
m=1
httrpp://ggitbb....mm//eedeed// ibration-tuningg
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Table 2: Hyperparameterss for LLM Training
Base Model Hyperparameters
LoRA Hyperparameters
Hyperparameter
Value
Hyperparameter
Value
Learning Rate
{10-5 10 10-0)
LoRA Rank
Batch Size
{1.4}
LoRA Alpha
16
Max Steps
10,000
LoRA Dropout
0.1
Optimizer
AdamW
Dropout Rate
0.0
Gradient Accumulation Steps
[1, 4]
Weight Decay
0.01
Gradient Clipping
1.0
Warmup Steps
500
Scheduler
Linear
where Bm is the set of predictions in bin (cc(Bm) is the accuracy, and conf(Bm)) is the average confidence of the predictions in that bin. ECE measures how well the model's calibrated.
predicted probabilities
Brier Score [BS; 45]:
N Σ 1=1
BS
N
where f; is the predicted probability and y; is the true label. BS combines both the accuracy and confidence of the predictions, penalizing overconfident and underconfident predictions.
Negative Log Likelihoud (NLL):
N
NLL
log p(ya xx)
N
where p(y xz²) the probability assigned the correct class yi given input æ;. NLL evaluates the model's probabilistic predictions and lower values indicate better calibration.
B.4 CalibRAG Details
Feature extraction details. To extract features for the forecasting function, we use the hidden state of the last token from the second-to- layer of the LLM M, as empirically yielded bet- ter calibration performance than other layers. This hidden state serves as the input representation fiea(g. d) for the classifier.
Positional encoding details. We use Fourier positional encoding with N frequency com- ponents to encode the temperature parameter t. This encoding covers the range E [1.0, 2.0], and during training data construction, we sample uniformly from this range simulate diverse user behaviors.
C Examples of query reformulations
In CalibRAG, the initial query is generated simulate how human decision-maker might pose simple query based on the input. For example, a decision-maker faced with problem such as "Is tomato a fruit or a vegetable?" might craft straightforward query like "Classification of tomatoes" to query a language model. Using this setup, we employed an LLM generator to create simple yet relevant queries and retrieved documents based on these queries. If the retrieved documents were insufficiently informative, the query was reformulated in Stage 3. This reformulation emphasized key terms to refine the query and improve the quality of retrieved documents. The specific prompt used for this process is detailed in G.
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Table Examples of Query Reformulation
Case
Original Query
Reformulated Query
Write paragraph about the effect TRR on my- ocardial contractility.
Write paragraph about the effect of Thyrotropin- Releasing Hormone (TRH) myocardial con- tractility.
Write a paragraph about the clinical trials for off- label drugs in nconates as cited in the literature.
Write a paragraph about clinical trials for off-label drug use in neonates as reported in the medical literature.
Write paragraph about the current representa- tives from Colorado.
Write paragraph about the tives from the state "Colorado" in the United States.
representa-
4 Write paragraph about the current minister of lo- cal government Zimbabwe and their role within government.
Write paragraph about the current Minister of Local Govemment and Public Works in Zim- babwe their within government.
Table 4: Effect Threshold Sclection on Performance. Experiments on BioASQ datasct show how creasing affects accuracy and calibration metrics.
AUROC
ACC
ECE
BS
0.0
71.21
0.83
35.03 0.14
0.2500 = 0.01
0.2900 + 0.01
0.4
76.15
1.50
35.05 0.25
0.2608 = 0.00
0.2830 + 0.00
0.5
76.50
4.98
35.98
0.38
0.2667 + 0.00
0.2779 + 0.01
0.6
77.20
4.10
36.50 0.45
0.2707 = 0.00
0.2800 + 0.01
To help readers understand the transformation from the initial query to its reformulated version, Table 3 provides examples that illustrate how queries evolve during the refinement process, offering practical insights into the mechanism.
D
Additional experiments
D.1
Analysis of
In our experiments, was set as a balanced choice to manage the trade-off between accuracy and cal- ibration error. As shown in Table increasing results in retrieving larger number of new queries, incorporating more relevant information, and thereby improving accuracy. However, this increase can potentially lead to higher calibration errors. Specifically, while better retrieval enhanced predic- tion accuracy, the confidence scores for these predictions only increased marginally. This mismatch between improved accuracy and relatively low confidence resulted underconfident predictions, which contributed to slight increase in calibration emror
To assess the impact of different values on model performance, we conducted experiments on the BioASQ dataset. Based on these observations, we selected = 0.5 as a reasonable compromise to balance accuracy improvements with calibration reliability.
D.2 Evaluation on BEIR Benchmark
To provide more comprehensive evaluation, we conducted experiments using two datasets from the BEIR benchmark: SciFact and TREC-COVID. These evaluations aim to validate the effective- ness of CalibRAG beyond its primary focus on well-calibrated decision-making, which predicts the probability of a correct decision when user relies on the generated guidance to solve given problem. While Ca not specifically designed as a reranking method optimize retrieval performance, it inherently supports both calibration and retrieval.
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Table 5: Evaluation results TREC-COVID and SciFact datasets, subset of the BEIR benchmark. The evaluation metric is Normalized Discounted Cumulative Gain (NDCG@K).
Model
Dataset
NDCG@5
NDCG@N 10
Cross-Encoder
TREC-COVID
0.7655
0.7576
SciFact
0.6668
0.6914
CalibRAG
TREC-COVID
0.7863
0.7660
SciFact
0.6872
0.7114
Table 6: Results of Verbalized Confidence Fine-Tune Evaluation on the MMLJU Dataset using
1.1ama-3 -3 1-81--88-11-- 1-s-Instrust. Evaluation metries are ACC and ECE.
Case
ACC
ECE
Continuous-Numn
43.63
0.3190
Discrete-Number
44.96
0.1605
Linguistic
45.03
0.1585
For the experiments, we followed the standard retrieval pipeline, retrieving documents using BM25 and reranking the top-100 results. We compared Aa with the Cross-Encoder baseline, and the results, presented in Table 5, demonstrate that Ca consistently outperforms the Cross- Encoder. These findings validate that CalibRAG not only enables well-calibrated decision-making but also enhances retrieval performance, reinforcing its utility in relevant scenarios.
D.3 Analysis of Verbalized Confidence Representations
CalibRAG does not rely linguistic numerical confidence in its primary approach. Instead, it provides confidence scores based on the probability predictions generated by the forecasting func- tion. Verbalized confidence, however, was used as baseline in comparative models. Verbalized confidence is typically expressed as a continuous number within the range [0, 100] Tian et al. [40] and Xiong et al. [52]. but LLMs often struggle to interpret these numerical values precisely.
To address this limitation, alternative representations were explored in the baselines: (1) linguistic expressions (e.g., "likely"), and (2) discrete numerical values ranging from 0 to 10. These ap- proaches were termed Linguistic and Number, respectively, with detailed prompt designs provided in Appendix E.
To further analyze verbalized confidence, conducted experiments on the MMLU dataset using the Llama-3-8B model. We evaluated the effectiveness of three confidence representations: continuous number, discrete number, and linguistic. As shown in Table 6, both discrete number and linguistic representations outperformed the continuous number baseline. Linguistic confidence, in particular, addressed the limitations of the model's 's understanding of numerical relationships and improved calibration.
D.4 Ablation on user model
We additionally conduct ablation evaluation on various user models U, considering that human users may make different decisions depending on their knowledge background real-world sce- narios. We evaluated the performance of CalibRAG and baseline methods the NQ and WebQA datasets using two retriever models, BM25 and Contriever. For this, we compare the performance of Phi -4 [58] and DeepSeek-Dii -iteee Distill [59], which represent state-of-the-art user models.
shown Fig. and Fig. 7, our results demonstrate that CalibRAG consistently achieves better accuracy and calibration error across different user models compared to other basclines.
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CT-Probe
Linguistic
Number
CallbRAG
Coneneverath
with
40C 40C BORLL 16111
40C LDB 6CC110 MCRLUT
M0 L4B 60C RCR
180 LLD MCR 10
(a)
(c)
(d)
Figure 6: Evaluation results baselines CalibRAG utilizing two retriever models: BM25 (a, b) and Contriever (c, d) on NQ (a, c) and WebQA (b, d). Here, we utilize Phi-4 [58] as our user model U. We report four metrics- Brrricss--AURRE --UROO, I-ACC, ECE, and Brier Score-where lower values indicate better performance.
CT-Probe
Linguistic
CalibRAG
Deopseels: 8M25 with
Despeasks
WebOA
Deeptent: Contrrleuor with Deepeneke Costriouer with Wesdh
0.2
(a)
(b)
(c)
(d)
Figure 7: Evaluation results baselines and CalibRAG utilizing two retriever models: BM25 (a, b) and
Contriever (c, d) on NQ (a, <) and WebQA (b, d). Here, we utilize DespSeek-Dist [59] as our user model U. We report four better performance.
I-ACC, ECE, and Brier Score-where lower values indicate
Table 7: Comparison of fine-tuned RAG reranking methods using our synthetic training data on HotpotQA.
Methods
AUROC (1)
ACC (t)
ECE (L)
BS (L)
Cross-encoder
60.74
34.98
0.477
0.477
Cross-encoder (Fine-tuned)
61.55
32.54
0.008
2.555
CalibRAG
72.47
42.37
0.106
0.206
Table Evaluation metrics of CalibRAG without reranking on WebQA using BM25
Methods
AUROC (t)
ACC (t)
ECE (L)
BS (L)
Number
69.38 + 2884
36.04 + 0.50
0.1931 0.0131
0.2293 0.0102
CalibRAG w/o Rerank
75.73 = 0.00
41.99 + 0.03 0.0780 + 0.0312
0.1981 + 0.0025
D.5
Ablation on Fine-Tuning for Reranking Baselines
To ensure fair comparison between CalibRAG and the reranking baseline, we also evaluated fine-tuned reranker model which fine-tuned using our synthetic datasets. However, discussed in 3.2, training was challenging due to the difficulty in feature extraction without using an embedding model to generate the guidance variable z. And this difficulty let fine-tuned model underfit to the training dataset. As shown in Table 7, the reranker model underperforms compared to the zero-shot setting. Therefore, in the Comparison with reranking and robust RAG baselines experiments in Sec. 4. we evaluated the CalibRAG model alongside zero-shot reranker models.
D.6 Ablation on CalibRAG without reranking
To isolate the effect of reranking our confidence calibration framework, we evaluated CalibRAG without using any reranking, where the model directly uses retrieved contexts without reordering them based on predicted confidence. shown Table 8, even without reranking. CalibR sub- stantially outperforms the Number baseline in both accuracy and calibration metrics. These results
27

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Table 9: Evaluation metrics of Number + Rerank and CalibRAG on WebQA
Retriever
Methods
AUROC (1)
ACC (1)
ECE (4)
BS (L)
Number + Rerank
75.06 + 0.00
42.42 + 0.01
0.2075 = 0.0167
0.2397 = 0.0109
BM25
CalibRAG
77.29 + 0.42
43.77 + 0.54
0.0567 + 0.0332
0.1983 = 0.0045
Number Rerank
76.84 + 0.00
43.08 + 0.00
0.2088 + 0.0127
0.23900 + 0.0083
Contriever
CalibRAG
76.24 + v.37
44.19 + 2.60
0.0997 + 0.0122 0.2095 + 0.0062
Table 10: Comparison of zero-shot evaluation of calibration baselines on NQ and WebQA datasets using BM25 (lexical) retrieval. Results are averaged over three random seeds.
Methods
NQ
WebQA
AUROC
ACC
ECE
BS
AUROC
ACC
ECE
BS
CT-LORA
73,51 1.88 37,70
2..8 0.2479 a ams 0.2709 1 0.003 74.36
1.17 38.09 + 6.28 0,2487 = 00303 0,2681 0,0700
CT-probe
00.92..:00
37.59
s0 0.3490
0026 0.3536
0.02 58.52
1:5 37.75
0.3491
0.3539 0.030
Linguistic-IoRA 57.12 4.35 39.42
0.94 0,4529 =: 0022
1 0.008 56.44
1.91 40.58 4: 1:18 :: 0.001 0.4385 0.0091
Number-LoRA
67.48 1: 1.42 34.38
0.7 0.1922 = 0.065 0.2294
1 0.00066 69.38 1.6 36.04
0.000 0.1931 +: 0,131 0.2293 0.0102
CalibRAG
77.29 0.12 42.66
0,9 0,0600
900099 0.1983 1 0.0017 77.29
0.44 43.77
1:044 0.0567 :: 00532 0,1983 0.0095
ColdRAGG-mdii 76.73 46.16
0.1397
0.2138
76.40
45.84
0.1372
0,2175
Table 11: Comparison of zero-shot evaluation of calibration baselines NQ and WebQA datasets using Contriever (dense) retrieval. Results are averaged over three random seeds.
Methods
NQ
WebQA
AUROC
ACC
ECE
BS
AUROC
ACC
ECE
BS
CT-LORA
69.89 4.94 39.93
1.36 0,2800 a unses 0,3008 1: 0.035 69.81 i 6.2 37,83 a 1.25 0,2646 : 00500 0,2860
0.0294
CT-probe
63.84 6.14 37,92
180 0.3225
00634 0.3343
0.098 62.65
8.10 36.43 a 4.0 0.3072 a: 00070 0,3180 0.056
Linguistic-LRRA
$7.05
0.37 0.4368
0.4252
39.76
0.4657 1: 0.4477
Nomber-LoRA
71.16 d 0,51 35.99
0.54 0.1827 =: 00124 0.2214
1: 0,0016
73:47
1.01 35.61 4 0.122 0.1754 +: 0.0124 0.2141 0,000
ColibRAG
73.89 à: 1.50 46.55
2.45 0.0312 n.075 0.2074
1: 0,0022
76.24
0.37 44.19 à 2.00 0.0970 :: 0,012 0,2095 0.0062
CalibRAG-midi
12.73 0.08 49.42
0,07 0.1656
0,019 0.2375
0,0013
72.95
0.08 46.78 0.02 0.1901 0,0022 0.2488 0.0009
indicate that the learned calibration itself, without requiring reranking, still provides significant ben- efit, demonstrating the robustness of CalibRAG's alignment mechanism.
D.7 Using Uncertainty baseline confidence scores for reranking
In this section, we investigate the effectiveness of using uncertainty baseline confidence scores for reranking in the RAG pipeline. As described in the main paper, these confidence scores are derived from verbalized scalar predictions generated by the LLM, typically representing values from 0 to 100.
While such scalar confidence values can be used to rerank retrieved documents, this approach in- curs significant computational overhead. Specifically, the Number baseline requires generating full guidance= 2 for every (g, d) pair before estimating confidence, as the model conditions on both the query and document generate scalar outputs. In contrast, CalibRAG directly estimates confidence from the (q, d) pair using a lightweight forecasting function f(g, d), thus avoiding this expensive intermediate generation.
Despite this additional cost, we performed an ablation to compare the reranking performance of Number-based confidence scores versus CalibRAG. As shown in Table 9, CalibRAG consistently outperforms the baseline across both B.M25 and Contriever retrievers on the WebQA dataset.
These results demonstrate that CalibRAG not only provides better-calibrated decisions but does so more efficiently without requiring guidance generation for every document candidate. This high- lights the dual advantage of CalibRAG in both performance and computational cost.
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Table 12: Comparison of zero-shot evaluation of calibration baselines BioASQ-Y/N, MMLU-Med, and PubMedQA datasets, Results are averaged over three random seeds.
Methods
Dataset
AUROC
ACC
ECE
BS
CT-LoRA
BioASQ-YA Y/N 65.20 2.32 54.31 0.73 0.5167 + 0.012 0.5099 = 0.0146
MMLU-Med 66.94 47.20 1.52 0.4293
0.4262
0.0084
PubMedQA 56.67 + 3.16 43.80 091 0.4307 0.009 0.4300 0.0034
CT-probe
BioASQ-Y/N Y/N 59.73 44277 57.98 + 145 0.5664 + 0.009 0.5630 = 0.0094
MMLU-Med 55.39 3.244 49.49
0.4771 0.0384 0.4758 0.0375
PubMedQA 54,56 1.6.1 46.60
1.8 0.4506 +: 0.012 0.4510 0.0121
Linguistie-LoRA RioASQ-Y/N 48.24 2.26 $7.82
0.3193
0.0027 0.3464 =
MMLU-Med 51.30 0.3 55.43 0.94 0.3262
0.3544 0.0049
PubMedQA 49.13 0.79 47.13 2:25 0.4047 0.0336
0,4225 0.021
Number-Lote Lonb RioASQ-Y/N 52.43 2.19 53.72 1.69 0.4664
0.8355
0.4659 = 0.0332
MMLU-Med 53.47 2:5 41.44
0.3394
0.3541
0.0135
PubMedQA 50.34 0.25 43.60
0.59
0.3866
0.0029
0.3954 0.0032
CalibRAG
BioASQ-Y/N + 1.34 70.82
3.34 0.2414
0,0427
0.2606
= 0.0386
MMLU-Med
68.93 1.32 57.20
0.0625
0.0653
0.2226
PubMedQA 66.57 2.00 62.20 3.53 0.2250 0.0353 0.2691 0.0072
Original question: The American Sweetgum the hostplant of what kind of bug?
Open-ended query: Write paragraph about the kind bug that uses the American Sweetgum as hast plant.
Original Retrieval Model's Top-1 document (This context the model's confidence 'Confident'):
American sweet gum, and other deciduous trees), and trapped in molasses baited jars. One researcher who collected specimens extensively found to be the most adaptable the "Parcoblatta" species, trapping adults among logs and undergrowth on the borders woodland areas, taking specimens trash and debris woodland campsights, and from homes wooded areas, which the species sometimes reported invade. Parcoblatta divisa Parcoblatta divisa, the southern wood cockroach, as species of cockroach native to the United States. User decision: American Sweetgum the host plant the Parcoblatta divisa, also known as the southern wood cockroach.
pasture grasses, grass under backyard trees,
dried cow dung, under
CaliBRAG's Top-1 document (This context the CalibRAG's confidence 81.41]:
sometimes as imitation mahogany or Circassian walnut. It used widely today in flake and strand boards. Sweetgum is foodplant varlous Lepidoptera caterpillars, such as the avpsy moth, American sweetgum is widely planted as an ornamental, within its natural range elsewhere. The hardened sap, gum resin, excreted from the wounds the sweetgum, example, the American sweetgum ("Liquidambar styraciflua"), can be chewed on like chewing gum and has been long used for this purpose in the Southern United States. The sap was also believed to be a cure for sciatica, weakness of nerves, etc. Sweetgum
The
plant gypsy moth.
Figure 8: Qualitative comparison of original retrieval model from CalibRAG.
D.8 Full numerical results for main experiments
Table 10, Table 11 and Table 12 present the complete numerical results from the primary experi- ments. For the Base model, we utilized pretrained model, sampling sentences across three different seeds. For the other methods, training was conducted across three random seeds to ensure robust evaluation. We highlight the best best-performing value in bold and the second-best in underline.
D.9 Qualitative Results
While quantitative metrics alone may not fully capture all the benefits of CalibRAG, we present examples highlighting its ability to identify relevant documents and assign calibrated confidence scores. Given the query "Write paragraph about the kind of bug that uses the American Sweetgum host plant.", the base retriever focuses only the keyword "American Sweetgum," retrieving
29

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loosely relevant content and marking its confidence as 'Confident' (10/11) as illustrated in Fig. 8. This led the incorrect conclusion that the sweetgum the host plant of Parcoblatta divisa, the southern wood cockroach. In contrast, CalibRAG captures the full context, retrieving documents specifically about the gypsy moth, which uses the sweetgum as a host plant, and correctly assigns a confidence level of 81.41. This demonstrates the capability of CalibRAG to find relevant docu- ment and assign a confidence level correlated with the accuracy of the downstream surrogate user. Additional examples can be found in E.
E
Data Examples
Fig. shows the top 20 examples of queries and their corresponding labels. The full set of data examples will be released upon publication of the paper. Fig. 9 shows that the ranking of the retrieved documents is not correlated with the accuracy of the user decision. As seen in this example, the top-ranked document not helpful for the user model in decision-making, whereas the second- ranked document provides information that can lead the user model to make a correct decision. This illustrates the importance of CalibRAG's forecasting function f in effectively modeling the probability that a decision made using document d is correct, emphasizing the need for reranking documents based on this modeling.
30

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Open-ended query: paragraph founding year Apple Computer.
million announced the company's emphasis the design and aesthetics of its products. 1999, Apple introduced the Power Mac G4, which utillzed the Motorola-made PowerPC 7400 containing a 128-bit instruction unit known as AltiVec, Its flagship processor line. Macintosh support
smash, moving
year.
Apple
public,
Also year, Apple unveiled the iBook, consumer-orien cereeeee-- laptop Wireless LAN via the optional AirPort card that based the 802.11b standard; helped (False)
Xcode: its online services include the ITunes Store, the IOS App Store and Mac App Store, Apple Music, and ICloud. Apple was Wayne April in January 1977, and sales its computers, including the Apple Il, quickly. Within
founded by Jobs, Steve Wozniak, was incerporated Apple Computer, few years, Jobs and Wozniak had hired staff of computer designers and had a production line. Apple went public in 1980 to instant financial success. Over next few years, Apple shipped new (True)
develop sell
Apple! personal computer.
had told him about and had said he needed money, Wozniak would have it to him. In 1975, Wozniak began designing and developing the computer that would eventually make him famous, the Apple I. On June 29 of that year, he tested his first working prototype, displaying few letters and running sample programs. was first in history that character displayed on generated home computer. With the Apple he and Jobs largely working impress other members the Palo Alto-based Homebrew Computer Club, (False)
at the Homebrew Computer Club. Apple was motherboard (with CPU, RAM, basic textual- chips), computer. The Apple
than what considered complete (S in dollars, adjusted for inflation). Apple Computer, Inc. was incorporated on January 3, 1977, without Wayne, who left and sold his share company back to Jobs and Woznlak $800 only couple weeks after co-founding Apple. Multimillionaire Mike Markkula provided essential business expertise and funding $250,000 during the incorporation of (True)
sale July 1976 and market priced $666.66
Applee. During the first five years of operations revenues grew exponentially, doubling about every four months. Between September 1977 and September 1980, yearly sales grew from $775,000 $118million, an average annual growth rate of 533%, The Apple invented Woznlak, major rivals, the TRS-80 and Commodore PET, because of its character cell-based color graphics and open architecture. While early Apple madels used ordinary cassette tapes as storage devices, they were superseded by the introduction of -nnch floppy (True)
introduced
1977, the
Computer
differed from
of "Kilobaud Microcomputing" publisher Weyne
Apple. prvention-getting and they must be prompting sale." August, the "Financial Times" reported that On December 12, 1980, any
that
Apple launched the Initial Public Offering of its stock to the investing public. When Apple went public, generated more capital than
any company history. Several venture capitalists cashed out, reaping billions long-term capital gains. January 1981, Apple held its first shareholders (False)
Company
instantly more
(about
with no programming language built-in. This presented problem to Apple: the Mac was due to be launched in 1983 (originally), with new user interface paradigm, but no third party software would available for it, nor could users easily write their own. Users nothing.
would to write simple applications fill these roles until third-party developers published more full-fledged software. The result was MacWrite and MacPaint, which shipped free with every Macintosh from 1984 to 1986. (False)
computer
this
upon
idea was design technology based on profile that included diskless computers, commonly coded applications using languages such and interface with the internet using partner in the network computing effort, and used the Apple Pippin platform its implementation. On July 9, 1997, Gil Amelio was ousted as CEO Apple by the board of directors. Steve Jobs stepped in the interim CEO ("ICEO", " as he was referred to), to begin critical restructuring of the company's product
software such Netscape Navigator. May
Apple became
would eventually
CEO (False)
Apple Writer Apple Writer word processor the Apple family of personal computers. published in 1979 by Apple Computer. Paul Lutus wrote "Apple Writer" alone in small cottage he built himself atop hill in the electricity grid cable strung trees. original 1979 version "Apple Writer"
created Paul Lutus and
woods Oregon, connected 13-sector DOS diskette and supported 4D-column text display. displayed text entirely uppercase, but could toggled pressing the ESC key; characters that the user (False)
Figure Top-20 retrieved document examples.
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Qualitative Examples
Here, we present additional qualitative examples for comparison with other baselines. In Fig. 10, Fig. 11, Fig. 12, and Fig. 13, the examples demonstrate that while the baselines retrieve documents that provide incorrect answers to the queries, they still assign high confidence to the retrieved docu- ments. In contrast, CalibRAG effectively reranks and retrieves documents that are highly relevant to the decision problem æ, allowing us to confirm that the guidance generated from these retrieved doc- uments is well-predicted to be helpful for decision-making. Additionally, we can confirm that when the document with the highest rank does not aid in decision-making for x, CalibRAG successfully assigns lower confidence level, helping to prevent the user from over-relying the guidance.
Original question: When was the American lawyer, lobbyist and political consultant who was a senior member the presidential campeign of Donald Trump born?
Answer:
Open-ended query: paragraph about the lawyer, lobbyist, and political consultant who was senior member of Donald Trump's presidential campaign, including details about when he was born.
Top-1 Page testified that he did not "directly" express support for lifting the sanctions during the meeting with Baranov, but that he might have mentioned the proposed Rosneft transaction. Carter Page Carter William Page |born June 3, 1971) is an American petroleum Industry consultant and former foreign-pollcy adviser to Donald Trump during 2016 Presidential election campaign. User decision: 1971.
Original
(This
LM model's confidence 'Certain'):
CaliBRAG's Top-1 document (Thss context set the CalibRAG's confidence to 83.93]:
Paul Manafort Paul John Manafort Jr. (bern April 1, 1949) is an American lobbyist, political consultant, lawver, and felon. A March 2015. and campalgn chairman from June August 2016, Manafort an adviser to the U.S. presidential campalgns Republicans Gerald Ford, Ronald Reagan, George H. W. Bush, and Bob Dole. in 1980, he co-founded the Washington, b.C-based lobbying firm Black, Manafort & Stone, along with principals and Rager Stone, joined by Peter Kelly Manafort labbied on User decision: April 1, 1949.
Republican, joined Donald Trumo's presidential campaign
Figure 10: CalibRAG vs Linguistie-LoRA. In the case of CalibRAG, a document about the person in question retrieved with a confidence level of 83.93%. In contrast, the document retrieved by the base retrieval model is related to Donald Trump but does not match the specific person in the query. Nevertheless, the Linguistic- LoRA model trust the document confidently.
Original question:
the allies after Africa?
Answer: Italy
Open-ended query:
Write paragraph about the next steps taken by the Allies following their involvement in North Africa.
Original Retrieval Model's Top-1 document (This context set the LM madel's confidence to 6/10):
completely Mediterranean theatre buildup and subsequent landing in French North Africa on November 1942, that known the allies Operation Torch. Not until the Tunisian Campaign was well under way did W-Leit 2 Taormina pull itself together and contribute to the general effort by intercepting air support messages. is worth noting that General Albert Praun, who was chief Signals of the Wehrmacht thought that the Allied Invasion of French North Africa User decision: Based the context provided, the Allies advanced towards Tunisia after North Africa.
Abteilung pracedures, subsequently
High Command
surprise bulk
detected due radio...
CallBRAG's Top-1 document (This context the CalibRAG's confidence 77.98):
1943, the Casablanca Conference, the Allies agreed demand Axis unconditional surrender, aim which implied the physical occupation of Germany with land farces. While building up strength for an invasion of continental Europe, the Allies pursued indirect strategy by invading Europe from the South. After defeating Axis forces North Africa [the invasion of French North-Africa), were invaded, leading
Sicily southern
Italy. Churchill especially favoured Southern strategy, aiming attack the "soft underbelly" Axis Europe through Italy, Greece and the Balkans in strategy similar to the Question: Write paragraph about the next steps taken by the Allies following their involvement in North Africa.
User decision: Allies went to Siclly.
Figure 11: CalibRAG vs Number-LoRA. In the case of CalibRAG, an accurate document about the location following North Africa was retrieved, allowing the model make correct decision. In contrast, the base retrieval model brought a different document. Nevertheless. Number-LoRA model set this context with confidence of 6 out of 10, leading the user to ultimately make an incorrect decision.
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Original question:
Who the first president to be impeached?
Open-endes query: paragraph about president impeached.
Original Retrieval Model's Top-1 document (This context the LM model's confidence 9/10): Constitution and the whole history of our republic demand that we make up our minds." The Judiciary Committee, with six proposed articles impeachment.
Republicans joining the Democratic majority, passed three Committee vated 27-11 recommend the first article of impeachment against the president: obstruction of justice. The Committee then recommended the second article, abuse of power, on July 29, 1974. The next day, on July 30, 1974, the Committee recormeended the third article: contempt of Congress, Article alleged in part: On June 17, 1972, and prior thereto, agents of the.. decision: Richard
July 27, 1974,
CalibRAG's Top-1
(This context
92.32):
justice following allegations that he committed perjury and obstructed justice conceal affair he had with Monica Lewinsky, 22-year old White House intern, Clinton was acquitted by the Senate in 1999 and completed his term in office. He is only the second U.S. president ever Impeached, the first being Andrew Johnson During last three years Clinton's presidency, the since 1969. In foreign policy, Clinton ordered U.S.
Congressional Budget Office reported budget surplus, first such military intervention the Bosnian and Kosovo wars, signed the Iraq Liberation Act in opposition Saddam Andrew Johnson.
User decision:
Figure 12: CalibRAG vs Number-LoRA. hase retrieval model facused solely on word 'impeached'
and retrieved a related document, missing the context of 'first.' Despite this, the Number-LoRA model set a confidence level of 9 out of 10, causing the user to make an incorrect decision. In contrast, CalibRAG retrieved accurate document that, while not explicitly containing 'first impeached, included the phrase 'first being.' confidence level 92.32%. allowing user to arrive at the correct answer.
Original question:
what year it for the Jewish calendar?
Open-ended query: Write paragraph about the current year the Jewish calendar.
Original Retrieval Model's Top-1 document (This context set the LM model's confidence 96.83): gates. Hebrew there
Hebrew letters. thousands, called ("major era"), and without the thousands, called ("minor era'). Thus, the current year written () using the "major era" and (X61000 using the "minor era". In 1178 CE, Maimonides wrote in the "Mishneh Torah", "Sanctification of the calculations
...of
common ways writing the year number: the
Moon' (11.16), that which is the 4938 of the...
chosen opoch from
as
day
present
User decision: According to the provided context, the current year in the Hebrew calendar is written 5784 (with the thousands) and 2444 (without the thousands), Given the model's high confidence level.
(This context ...year; and about every 231 it will fall day behind the Gregorian calendar year. The used since the Middle Ages is the epoch (Latin for "in the year of the world"; "from the creation of the world"). As with (A.D, or AD), the words or abbreviation (A.M. AM) the era should properly "precede" the date rather than follow it. AM began sunset on and will end sunset The lewish
CalibRAG's Top-1
CalieRAG's confidence 20.95):
no fixed length. The Jewish User decision: I'm not able to provide the current in the Jewish calendar based on the given context and the model's confidence level. The model's confidence level is 20.95, which below.
modeled the reference "...there
evening and...
Figure 13: CalibRAG ys CT-LoRA. In the case of CalibRAG, the top-20 confidence score 20.95 for incorrect information, causing the user to hesitate in making decision. However, with the CT-LoRA model, incorrect information is assigned confidence of 96.83, leading the make incorrect decision.
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G Prompt Examples
In this section, present prompt examples used during training and inference. Figure 14a shows the prompt that encourages the user model U to act like human decision-maker leading it to over- rely on the guidance provided by the LLM. Figure 14b displays the prompt that generates the open- ended query q from the decision task Figure 14c presents the prompt that induces the generation of guidance from based on the retrieved document Figure 15a used when grading the user model U's decision against the true answer using G. Figure 16a, Figure 16b, and Figure 16c are prompts used to instruct M to generate confidence terms of linguistic or numerical calibration. Lastly, Figure 15b is the prompt used during Stage 3 of the inference process.
Decision prompt
The task is to answer questions based context generated language model in response to question about relevant information, along with the model's confidence level in the provided answer.
Context: (context)
Question: (question]
Model Confidence: (confidence)
Answer:
Prompt designed to guide the user model U in making decisions based on the LLM-gemerated guidance and confidence c.
Prompt that generates open-ended query q from the decision task
You are an auttma assistant tasked with rephrasing specific questions into open-ended queries to encourage detailed exploration and discussion of the key tepics mentioned.
Your goal is to prompt someone to write paragraph exploring the topic without directly revealing the answer. Examples for Guidance:
Example
Question 1: Which sea creature the world's largest invertebrate?
Question 2: Write a paragraph about the world's largest invertebrate.
Now, please rephrase the follewing question: Question 1: (question)
Question 2:
(b) This prompt was first suggested by Band et al. [6]. and we have modified part of the proposed prompt for use here. We this prompt input when generating the query based the decision task æ.
Guidance generation prompt
Directly state the answer without phrases like tthe correct answer is.
Given the retrieved context, answer the questien as accurately possible,
Question: (question)
Retrieved Context: (title) [context)
Answer:
(c) This prompt guides the LLM M to provide direct, concise guidance based on given retrieved document d.
Figure Prompt used for (a) user model making decisions, (b) generating from z, (c) generating
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Evaluation prompt
The problem is: lquestica) The correct answer for this problem is: (ground-truth)
student submitted the answer: (prediction) The student's a answer must be correct and speci fic but not overcomplete (for example, if they provide two different answers, they did not get the question right).
However, small differences in formatting should not be penalized (for example, 'New York City' is equivalent to 'NYC'). student provide an equivalent answer to the ground truth? please answer yes or no without any explanation:
Did the
(a) Prompt used to evaluate the long-forn form generated answer.
Query regeneration prompt.
You are a language mocel assistant who specializes improving queries for document search systems. Your task is to highlight and clarišy the important parts of given query to make it more precise and help retrieve relevant documents.
Flease take emphasizing the important words. Do not add any new information not included in the original query.
original search query below and rewrite by
Original Retrieval Query: (query)
Please generate the retrieval query without any explanation:
(b) This prompt assists rewriting search queries enhance precision and relevance for document retrieval, emphasizing the crucial elements without adding extraneous information.
Figure 15: Prompt used for (a) evaluation and (b) query regeneration.
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Calibration tuning prompt
the proposed answer correct?
Choices:
(i): no
(ii): yes
Answer:
(a) This prompt first suggested by Kapoor al. [21]. poses straightforward question verify the correctness proposed answer with binary choices for evaluation. used this prompt training our baselines.
Linguistic calibration prompt
Provide the certainty level of answer using given 11 certainty levels. Give ONLY your certainty level, no other words or explanation.
Certainty Levels: Unlikely, Doubtful, Uncertain, Ambigucus, Probable, Likely, Possible, Specified, Confirmed, Certain, Inevitable.
For example: Certainty: <ONLY the certainty level that Answer is correct, without any extra commentary whatsoever; just the certainty level!> Certainty:
(b) This prompt requires the model to evaluate the certainty of answer using predefined set of linguistic levels of certainty. We used this prompt for our baselines that utilize linguistic calibration.
Number calibration prompt
Provide the certainty level of answer using the given 11 certainty levels. Give ONLY your certainty level, other words explaration.
Certainty levels: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.
For example: Certainty: <ONLY the certainty level that Answer is correct, without any extra commentary whatsoever; just the number!> Certainty:
(c) This prompt is similar to the linguistic calibration prompt but uses numerical certainty levels (from to 10) to rate the confidence in the answer provided. We used this prompt for our baselines that utilize number calibration.
Figure 16: Prompt used for baseline experiments.
36


Reviews:
[{"review_id": "nDkGaogJEC", "reviewer": "Reviewer_FRsE", "summary": "This paper studies to calibrate the decision-making process for retrieval augmented generation (RAG). The paper trains a forecasting function which takes a query-document pair and a temperature parameter representing user behavior. The model is trained on data generated by the LLM with different temperature setup. Experiments show that, not only the trained model can better calibrate the results, but it can also be used for context selection for RAG.", "questions": "- I wonder what the stop criterion is for the inference iterations? Or if step 3 will only be executed once, then what if there is still no quality retrieved document?\n- Any intuitions on using Fourier positional encoding for temperature parameter? Are there other potential choices?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The calibration problem for RAG is interesting and has its implications in real applications. It is interesting to see how confident/uncertain the LLM is based on the provided context and whether the response will be helpful.\n- The proposed method is also sound and innovating. The usage of temperature to simulate user behavior and help data generation and training seems to be a promising approach.\n- Some of the tricks and observations made by this work could motivate more research in similar directions.\n- The paper is clearly written. I like the example in figure 2 which clearly demonstrates the task and motivation.\n\n### Weaknesses\nOverall I think it is a nice work and I didn't notice any overwhelming weakness.\n- It would be beneficial to include some related works on RAG research in the background discussion.\n- Some more discussions on limitations and future works could be beneficial for this paper as well as future researchers.", "was_revised": true, "final_justification": "Overall, the paper studies a meaningful task and proposes a novel and solid approach. My concerns are addressed well during rebuttal and the explanation should be included in the next version.", "rebuttal": ">**[W1, W2] Can the paper provide a clearer positioning within existing RAG literature and discuss limitations and future directions more explicitly?**\n\nThank you for the thoughtful suggestions. While we have included related RAG works in Appendix A.2, we recognize that their placement may reduce accessibility. We will revise the introduction to more clearly discuss RAG-related literature and better contextualize our contribution. Additionally, we will update the conclusion to include a concise discussion of limitations and potential future directions to support ongoing research in this area.\n\n>**[Q1] I wonder what the stop criterion is for the inference iterations? Or if step 3 will only be executed once, then what if there is still no quality retrieved document?**\n\nThank you for the insightful question. To ensure a fair and consistent comparison across all methods, we did not perform Step 3 in any setting except for Figure 5-(c). In that experiment, we applied query reformulation only once, explicitly to evaluate its potential impact. \nWe also note that query reformulation is an optional module in our pipeline. In real-world deployments, users can decide whether to enable it based on the trade-off between computational cost and performance gain.\nThat said, we agree that if the reformulated query still results in low-quality retrieved documents, a single iteration may be insufficient. This limitation stems from both the inherent recall limitations of the retriever and the limited representational coverage of the document pool.\nTo address this, incorporating adaptive multi-step retrieval mechanisms, possibly coupled with uncertainty-aware stopping criteria, is a promising direction for future work. We will clarify this detail in the final version of the paper.\n\n>**[Q2] Any intuitions on using Fourier positional encoding for the temperature parameter? Are there other potential choices?**\n\nThank you for the question. Using a scalar $t$ directly often limits representational power and gradient flow, making it difficult for the model to condition on user behavior effectively. To address this, we adopt Fourier positional encoding, which maps $t$ into a high-dimensional space using sinusoidal functions. This approach is widely used in neural fields (e.g., NeRF [1]) to inject continuous inputs into deep models and enables smoother generalization across varying $t$. We found it effective and lightweight, though other encodings (e.g., learned embeddings) are also possible.\n\n\n\n[1] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis., Ben Mildenhall, et al., ECCV 2020."}, {"review_id": "gGRyaTzeSP", "reviewer": "Reviewer_gigS", "summary": "This paper proposes CalibRAG, a method to align the confidence level of Retrieval-Augmented Generation (RAG) systems with actual correctness.  It first employs an LLM-based surrogate user model to create probability labels about what proportion of answers (sampled outputs conditioned on a temperature) with the retrieved documents are correct. Then, it trains a parameterized forecasting model that predicts the probability. In the inference stage, CalibRAG utilizes the forecasting model to rerank the retrieved documents and select the highest-ranked document for final generation. CalibRAG improves both accuracy and calibration performance over baselines in various QA datasets.", "questions": "## Questions\n\n1. Is it possible to show the performance of CalibRAG on other types of \"decision-making\" tasks or real-user experiments? I would say **the limited evaluation (on QA tasks) is my main concern** since one can argue that in QA tasks, a RAG model can give answers as well, so it is not a necessity to have a \"user model\".\n2. In the inference stage, you only select the top-1 document for the final generation. However, current RAG models (w/ long-context ability) can **process much more than 1 document, and it may naturally utilize the most useful information**, making the reranking less necessary. Can you provide some results about using more retrieved documents?\n3. L263: For baselines, guidance and confidence are generated based on the top-1 document retrieved. Is it fairer to generate the confidence scores for all $K$ documents and utilize the most confident one?\n4. As you use GPT-4o-mini as the evaluator in both your forecasting model training and final judgment, the forecasting model can somehow overfit (not sure, just guessing). Could you use another evaluator in the final judgment to show the robustness of CalibRAG if possible?\n5. What is the reason for not taking guidance $z$ as one of the input params of the forecasting model? Is it because of efficiency? As your ultimate goal is to predict **how $z$ impacts decisions**, using $q$ and $d$ as substitutes seems suboptimal, as $z$ also incorporates the inherent knowledge of RAG models.\n6. Why do you use Llama-2-7B in Table 1b, not aligned with the main setup (Llama-3.1-8B)?\n7. L342: \"gains diminish beyond 40 documents\". However, it seems that using 40 documents yields larger gains compared to 20. Can you clarify this statement?\n8. What model is used for the query reformulation? What are the maximum reformulation times when the threshold cannot be satisfied?\n\n## Suggestions\n\nTypos in writing:\n\n1. L224: $t$.\n2. L250: Change \"decision model\" to \"user model\".\n3. Calibrag (should be CalibRAG) in Figure 5c.\n\n[1] may be a missing related work (or baseline).\n\n[1] Zhao, Xinran, et al. \"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models.\" Findings of ACL 2024.", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. The overall method design is reasonable. Using decoding temperature to reflect user behavioral traits is interesting.\n2. The baselines included in the comparison are representative and comprehensive.\n\n## Weaknesses\n\n1. The evaluation in this paper is mainly about QA tasks, while decision-making (highlighted by this paper) may not only include QA.\n2. All experiments consider using the top-1 document in the final generation, which seems a little bit far from the mainstream RAG pipeline.", "was_revised": true, "final_justification": "The authors have largely addressed my concerns regarding the QA evaluation by providing a human study and presenting results using more RAG documents, where CalibRAG still demonstrates advantages. Overall, the method is reasonable and novel, with potential value to the field.", "rebuttal": ">**[W1, Q1]  Can CalibRAG demonstrate effectiveness on decision-making tasks beyond QA or in real-user scenarios?** \n\nWe agree that QA tasks alone may not sufficiently capture the broader notion of decision-making, as direct answering can reduce the role of a user model. To address this, we designed our setup to simulate decision-making: instead of answering the original question $x$, CalibRAG generates an open-ended query $q$ to retrieve information relevant to user decisions (L195–L196). This aligns with recent work on LLM-based uncertainty calibration and decision support [1, 2].\n\nWe acknowledge that surrogate user models cannot fully replicate real human behavior. To mitigate this, we employed diverse LLMs including LLaMA 2 7B (Table 1-(b)), Phi 4, and DeepSeek Distill (Appendix D.4) to reflect varying user backgrounds and preferences.\n\nAdditionally, we conducted a human evaluation to assess how closely CalibRAG's judgments align with human decisions. Specifically, we sampled 2 questions per confidence bin across 5 bins from WebQA, NQ, and BioASQ (10 examples total), and asked 10 human raters whether they agreed with the user model’s judgment (i.e., whether the retrieved document was helpful). The result was an average agreement rate of 81.33%, suggesting strong alignment between surrogate model outputs and human intuition.\n\nTogether, these quantitative and qualitative results demonstrate that CalibRAG goes beyond standard QA and effectively supports realistic decision-making scenarios.\n\n[1] Band, Neil et al. \"Linguistic Calibration of Long-Form Generations.\" ICML, 2024.\n\n[2] Kapoor, Sanyam et al. \"Large Language Models Must Be Taught to Know What They Don’t Know.\" NeurIPS, 2024.\n\n>**[W2, Q2] Recent RAG models can handle multiple documents at once. Why did you use only the top-1 document for generation? Wouldn’t using more documents reduce the need for reranking? Do you have any experiments evaluating this?**\n\nThank you for the insightful question. In response to your suggestion, we conducted additional experiments to compare the performance of CalibRAG and the baseline (Number) in a multi-document RAG setting.\n\nSpecifically, we used the BM25 on the WebQA, with Llama-3.1-8B-Instruct as the user model. In this setup, we provided the LLM with the top-k documents selected either by the retriever (baseline) or by CalibRAG’s confidence-based reranking, and generated final answers based on the combined input. As shown in Table R.1, even when using k=3 documents, CalibRAG consistently outperforms the baseline in terms of ACC as well as calibration metrics.\n\nDue to resource constraints, we limited our evaluation to k ≤ 3. Within this range, we observed that increasing k improves ACC for both methods. However, prior work [1] has shown that in multi-document RAG settings, the order of documents significantly affects performance, and [2] further reports that providing more than three documents may introduce contradictory information, which leads to increased noise and degraded performance. These findings suggest that simply adding more documents does not necessarily lead to better outcomes. **Instead, effective document selection and reranking remain essential.**\n\n\n__Table R.1__ Comparison of performance in multi-document generation settings.\n\n| \t\t           |  AUROC  |  ACC  |  ECE  | BS |  \n|-|-|-|-|-|\n| Number   (k=1)         |   69.38    |   36.04    |    0.1931   |   0.2293   |\n| Number   (k=2)         |   66.46   |    38.90   |   0.2580    |   0.2888   |\n| Number   (k=3)         |   64.61    |    41.28   |   0.2907    |   0.3204   |\n| CalibRAG   (k=1)      |    77.29   |   43.77    |   0.0567    |    0.1983   |\n| CalibRAG   (k=2)      |   75.47    |   42.86    |   0.0557    |   0.2029    |\n| CalibRAG   (k=3)      |    76.75   |   43.28    |   0.0655    |   0.1999    |\n\n[1] Liu, Nelson F., et al. \"Lost in the Middle: How Language Models Use Long Contexts.\" TACL., 2024.\n\n[2] Kim, Jaehyung, et al. \"SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs.\" ICLR., 2024.\n\n\n>**[Q3] L263: For baselines, guidance, and confidence are generated based on the top-1 document retrieved. Is it fairer to generate the confidence scores for all documents and utilize the most confident one?**\n\nAs noted in L299–L301, we would like to emphasize that **we have already conducted this experiment.** As described in Appendix D.7, we explored an extended baseline where decoder outputs $z$ are generated for all retrieved documents. A separate confidence model is then used to predict confidence scores for each document, and the one with the highest score is selected. While this approach is conceptually valid, it suffers from significant computational overhead due to the need to generate responses for all documents. Despite this additional cost, the calibration performance of this baseline remained inferior to that of CalibRAG.\n\n__Table 9.__  Evaluation metrics of Number + Rerank and CalibRAG on WebQA\n| Retriever | Methods         | AUROC        | ACC         | ECE        | BS         |\n|-|-|-|-|-|-|\n| BM25      | Number + Rerank     | 75.06 | 42.42| 0.2075 | 0.2397 |\n|               | CalibRAG            | 77.29  | 43.77  | 0.0567| 0.1983   |\n| Contriever| Number + Rerank     | 76.84      | 43.08    | 0.2088   | 0.2390   |\n|               | CalibRAG            | 76.24  | 44.19  | 0.0997 | 0.2095 |\n\n*For completeness, we summarize the results from Appendix D.7-Table 9 again here.*\n\n>**[Q4] Could you use another evaluator in the final judgment to show the robustness of CalibRAG if possible?**\n\nThank you for the suggestion. Table R.2 reports results on the NQ using BM25, and the evaluator used for decision scoring is LLaMA-3.2-3B-Instruct. We acknowledge the value of evaluating robustness across different evaluators and plan to include results on additional tasks with alternative models in the final version.\nNonetheless, the clear and consistent improvements observed in all metrics indicate that CalibRAG exhibits robust calibration performance under the current setting.\n\n__Table R.2__ Evaluation on NQ dataset using BM25 and LLaMA-3.2-3B-Instruct as the evaluator.\n| Setting   | AUROC   | ACC     | ECE     | BS |\n|-|-|-|-|-|\n| CT        | 0.5201  | 0.4406  | 0.3104  | 0.3510       |\n| CT-Probe  | 0.6217  | 0.3963  | 0.3188  | 0.3426       |\n| Linguistic| 0.5964  | 0.4453  | 0.4262  | 0.4109       |\n| Number    | 0.6484  | 0.4312  | 0.3105  | 0.3367       |\n| Calibrag  | 0.7436  | 0.5196  | 0.0963  | 0.2204       |\n\n\n\n>**[Q5] What is the reason for not taking guidance  as one of the input params of the forecasting model?**\n\nAs you noted, our goal is to predict how the generated guidance $z$ affects user decisions. While explicitly generating $z$ for every $(q, d)$ pair is computationally expensive, our model addresses this by using the internal representations of the LLM that generates $z$ as features (L160–161, L168), allowing it to capture $z$'s influence implicitly. This design preserves efficiency, and its effectiveness is validated by strong empirical performance.\n\n>**[Q6] Why do you use Llama-2-7B in Table 1b, not aligned with the main setup?**\n\nWe used LLaMA-2-7B in Table-1-(b) for two main reasons. \nFirst, the Self-RAG model is only publicly available with LLaMA-2-7B and 13B, so we used the same base model to ensure a fair and reproducible comparison. \nSecond, we aimed to show that CalibRAG performs robustly across different backbone architectures. The results in Table-1-(b) confirm that CalibRAG remains effective even when used with a different LLM. \n\n>**[Q7] L342: \"gains diminish beyond 40 documents\". However, it seems that using 40 documents yields larger gains compared to 20. Can you clarify this statement?**\n\nThank you for the observation. We have conducted additional experiments on both WebQA and NQ using BM25 and Contriever as retrievers. Across the majority of these settings, we observed that performance gains tend to saturate beyond the top-20 retrieved documents. The statement in L342 was based on this general trend. However, Figure 5-(b) happens to show one case (WebQA with Contriever) where the performance improvement from 20 to 40 documents appears more pronounced, which may have caused the perceived inconsistency. We will revise the text and figure to clarify that gains generally diminish after 20 documents across most settings and support this with a more complete set of results in the final version.\n\n__Table R.3__ Performance of CalibRAG across different numbers of retrieved documents.\n| Setting / # Documents |   5   |  10   |  20   |  40   |\n|-|-|-|-|-|\n| BM25 - NQ             |  42.96     |   42.22   |   43.77    |   43.68    |\n| BM25 - WebQA          |   42.21    |    42.40   |   42.66    |   42.70    |\n| Contriever - NQ       |    44.27   |   45.05    |   46.55    |   46.07    |\n\n>**[Q8] What model is used for the query reformulation? What are the maximum reformulation times when the threshold cannot be satisfied?**\n\nThank you for the question. The model used for query reformulation is the same LLM used to generate the guidance $z$. We perform query reformulation only once in the experiment shown in Table 2-(c), where we explicitly evaluate its effect. In principle, query reformulation can be applied repeatedly based on user configuration or an adaptive stopping criterion. However, in our main experiments, we did not apply reformulation at all in order to ensure fair and consistent comparison across all methods. We will clarify this implementation detail in the final version.\n\n**Additionally, thank you for the helpful suggestions. We will incorporate Zhao et al. (2024) into the related work and correct all noted typos (L224, L250, Figure 5c) in the final version.**"}, {"review_id": "v29edyMK2G", "reviewer": "Reviewer_urW9", "summary": "The paper proposes to add calibration estimation to RAG outputs. CalibRAG incorporates a forecasting function—a surrogate model—that predicts the probability that a user's decision, informed by the retrieved information, will be accurate. They generate synthetic training data to teach the forecasting model to align confidence with actual decision outcomes and integrate it into the retrieval and response pipeline.", "questions": "na", "limitations": "na", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThe ideas is simple and introduces a clear extension borrowing from far enough field to this field.  \nThe writing is clear, and the presentation is appealing.\nThe papers' scores seem high and the performance convincing.\nThe paper uses multiple baselines and retrievers to give a more round evaluation and validate the generality of their method.\n\nWeakness: \nThe idea sounds very clean, so much so that it is surprising no one did it, despite so much RAG works. A search on my side didn't find something like that though.  \nI see the dataset as quite a minor contribution, but it is not a negative to have *also* a minor addition.\n\nMinor:\nThe people in the figure are pretty, but maybe keeping mostly they faces would make the differences between them more salient?\nIn the contributions paragraph, references to where each part is written could be useful for people to jump to the right place (e.g., the dataset is not reflected in the section structure).", "was_revised": true, "final_justification": "It is borderline mainly because it sounds ok but not especially exciting or unusual and my low confidence in the field. But, it seems al reviewers lean towards accepting it, so can be accept as far as I am concerned.", "rebuttal": ">**[W1] Is the idea truly novel, given its simplicity, and is the dataset contribution substantial enough to strengthen the paper's overall impact?**\n\nThank you for the kind feedback. We also found it surprising that prior RAG research has largely overlooked decision-aware confidence calibration, especially given the importance of reliability in downstream applications. We believe this is due to the lack of clear formulation and evaluation settings for decision-centric RAG scenarios, which our work aims to establish. We appreciate your recognition that our dataset, while a smaller contribution, serves as a concrete step toward enabling standardized evaluation in this space.\n\n>**[W2] The people in the figure are pretty, but maybe keeping mostly their faces would make the differences between them more salient? In the contributions paragraph, references to where each part is written could be useful for people to jump to the right place (e.g., the dataset is not reflected in the section structure).**\n\nThank you for the helpful suggestions. We will revise the figure to focus more clearly on the distinctive facial features, which better convey diversity among user types. Additionally, we will edit the contributions paragraph to include references to the relevant sections for each component, including the dataset, to improve navigability and clarity in the final version."}, {"review_id": "E5MXmSCzGM", "reviewer": "Reviewer_xGBU", "summary": "This paper introduces CalibRAG, a Retrieval-Augmented Generation (RAG) framework designed to produce not only relevant evidence for decision-making but also well-calibrated confidence estimates for those decisions. Traditional RAG approaches often yield overconfident guidance, whereas CalibRAG trains a forecasting function that, given a prompt, a retrieved document, and a model temperature setting, predicts the probability of a correct decision. Empirical results across several question-answering tasks and retrieval back ends demonstrate that CalibRAG improves decision accuracy and calibration metrics relative to standard baselines.", "questions": "1. Do you foresee applying CalibRAG to other generative tasks such as summarization or code generation?\n2. Retrieving and reranking a sizable set of documents and performing query reformulation adds computational cost. Have you analyzed the detailed inference time overhead?", "limitations": "See weakness above", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper clearly articulates the issue of overconfident retrieval in RAG systems, using intuitive examples that ground the reader in the practical need for decision calibration.\n2. Integrating a temperature-aware forecasting function into the retrieval step is novel and makes sense.\n3. The evaluation spans varied question-answering benchmarks and diverse retrieval back ends.\n\nWeakness:\n1. Retrieving and reranking a sizable set of documents, with potential query reformulation, may introduce non-trivial latency.\n2. The forecasting function is trained on synthetic data derived from a surrogate language model, leaving open questions about how well it mirrors real human decision behavior.", "was_revised": true, "final_justification": "I have read the rebuttal, which has fully resolved my concerns. I decide to keep my score positive.", "rebuttal": ">**[W1, Q2] Retrieving and reranking a sizable set of documents, with potential query reformulation, may introduce non-trivial latency. Retrieving and reranking a sizable set of documents and performing query reformulation adds computational cost. Have you analyzed the detailed inference time overhead?**\n\nRecent LLM-based reranking methods [1,2,3] typically require feeding multiple documents into the LLM simultaneously to assess their relative importance. This involves generating token-level outputs over all N documents, making the comparisons relative and thus difficult to parallelize. Moreover, such approaches are sensitive to the context length limitations of LLMs. As a result, their time complexity is approximately O($N^2⋅L^2+NL^2$), where L is the average document length, and if we assume that the length of the generated token is also L.  \n\nIn contrast, CalibRAG independently processes each query-document pair ($q$, $d_i$) to predict a confidence score. Here, the theoretical time complexity for the CalibRAG is O($N⋅L^2$), which is smaller than the recent LLM-based reranking methods. Furthermore, the confidence score is computed via a lightweight MLP without requiring autoregressive decoding, making it substantially faster and more efficient. \n\nTo support this, we provide a direct comparison of inference time between CalibRAG and LLM reranking using the same document set in Table R.1, with interpretation to be included in the final version.\nMeanwhile, query reformulation is an optional component. For the sake of fair and consistent comparisons, we did not apply it in most experiments. It is only used once in Figure 5-(c) to explicitly assess its effect. In practical deployment, users may choose to apply reformulation depending on their desired trade-off between performance and computational cost.\n\nIn summary, CalibRAG is designed for efficient document reranking and confidence estimation, and demonstrates clear advantages in terms of inference efficiency.\n\n__Table R.1__ Inference time comparison between CalibRAG and LLM-based reranking. We used Llama-3.1-8B-Instruct and measured the average inference time over 20 randomly selected queries from the NQ dataset. CalibRAG evaluates each (query, document) pair independently and in parallel, while LLM-rerank jointly processes all documents via ~500-token generation. Measurements were taken on a single A6000 48GB GPU.\n\n| Method | Time per document set\n-|-\nLLM-rerank | ~10 minutes |\nCalibRAG (avg) |~1.7 minutes |\n\n[1] Zhuang, Shengyao, et al. \"A setwise approach for effective and highly efficient zero-shot ranking with large language models.\" Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024.\n\n[2] Qin, Zhen, et al. \"Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting.\" Findings of the Association for Computational Linguistics: NAACL 2024. 2024.\n\n[3] Kim, Jaehyung, et al. \"SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs.\" The Twelfth International Conference on Learning Representations.\n\n>**[W2] The forecasting function is trained on synthetic data derived from a surrogate language model, leaving open questions about how well it mirrors real human decision behavior.**\n\nThank you for the thoughtful comment. While it is true that the forecasting function is trained on synthetic data generated from a surrogate language model, we would like to highlight several key points that support the practicality and reliability of our approach.\n\nFirst, we conducted a human evaluation to assess how well the model’s predictions align with actual human decisions. Specifically, we randomly sampled 2 questions per confidence bin across 5 bins from WebQA, NQ, and BioASQ (totaling 10 examples), and presented the question and retrieved context to 10 human annotators. They were asked whether they agreed with the user model’s judgment—that is, whether the retrieved context was helpful for answering the question. The results showed an agreement rate of 81.3%, indicating that the surrogate-based forecasts meaningfully reflect human judgment.\n\nSecond, conducting full-scale user studies where humans serve as decision-makers in downstream tasks is logistically challenging and costly. As an alternative, we simulate diverse user preferences by evaluating CalibRAG under multiple distinct surrogate user models (e.g., Phi-4, DeepSeek-Distill), which reflect different decision criteria and levels of background knowledge. As shown in Appendix D.4, CalibRAG consistently outperforms baselines across these varied simulated user types, demonstrating robustness to user variability.\n\nThird, although our current setup relies on LLM-based simulations, it can be naturally extended to incorporate real user data. For example, future work could fine-tune the forecasting model using expert annotations or crowd-sourced feedback to enhance realism and generalization.\n \n>**[Q1] Do you foresee applying CalibRAG to other generative tasks such as summarization or code generation?**\n\nThank you for the question. We believe CalibRAG can be extended to other generative tasks such as summarization or code generation, as long as a meaningful retriever can be constructed. For summarization, this applies in settings where specific summary formats are desired and can be retrieved. For code generation, the applicability depends more on retrieving relevant algorithmic patterns, which is feasible but more domain-specific. Importantly, although our model was trained on general-domain data, it already shows strong generalization to out-of-domain tasks (e.g., medical QA), suggesting broader applicability of our framework."}]

Author rebuttal:

Meta-review: This paper introduces CalibRAG, a method for aligning confidence estimates in retrieval-augmented generation systems with correctness of responses. The key idea is to model user risk tolerance via sampling temperature, using an LLM to simulate decision-making under different tolerances. A forecasting model is then trained to predict calibrated confidence scores conditioned on the query, retrieved document, and sampling temperature, and is applied at inference to select documents that maximize calibration.

Reviewers found the approach sound and innovative (FRsE, gigS, xGBU), with clear exposition (urW9), comprehensive baselines (gigS, urW9), and a dataset that may benefit the broader community. Concerns raised during review included potential latency overhead (xGBU), questions about how well the proposed framework mirrors real user decision-making (xGBU, gigS), and possible bias in evaluation (gigS). However, reviewers agreed that these issues were addressed convincingly in the rebuttal. Incorporating rebuttal clarifications regarding latency, decision modeling, and evaluation fairness would strengthen the final version of the paper.
Decision: Accept (poster)