SteerConf: Steering LLMs for Confidence Elicitation
Authors: Ziang Zhou
Tianyuan Jin
Jieming Shi
Li Qing
Venue: NeurIPS 2025 poster ()
Keywords: calibration
uncertainty
prompting
black-box
LLM
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: We introduce SteerConf, a method to steer the verbalized confidence of LLMs and calibrate these confidences, enhancing their reliability and trustworthiness in practical applications.

Abstract: Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence, limiting their reliability in critical applications. We propose SteerConf, a novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability. SteerConf introduces three key components: (1) a steering prompt strategy that guides LLMs to produce confidence scores in specified directions (e.g., conservative or optimistic) by leveraging prompts with varying steering levels; (2) a steered confidence consistency measure that quantifies alignment across multiple steered confidences to enhance calibration; and (3) a steered confidence calibration method that aggregates confidence scores using consistency measures and applies linear quantization for answer selection. SteerConf operates without additional training or fine-tuning, making it broadly applicable to existing LLMs. Experiments on seven benchmarks spanning professional knowledge, common sense, ethics, and reasoning tasks, using advanced LLM models (GPT-3.5, LLaMA 3, GPT-4), demonstrate that SteerConf significantly outperforms existing methods, often by a significant margin. 
Our findings highlight the potential of steering the confidence of LLMs to enhance their reliability for safer deployment in real-world applications. The implementation is at \url{https://github.com/scottjiao/SteerConf}.

Steering LLMs for Confidence Elicitation
Ziang Zhou
Tianyuan Jin
Department of Computing
Department of Electrical and Computer Engineering National University of Singapore Singapore tianyuan@nus edu.sg
The Hong Kong Polytechnic University Hong Kong, China Eduscottzhou@@ttlookkk com
Jieming Shi"
Qing Li
Department of Computing
Department of Computing
The Hong Kong Polytechnic University Hong Kong, China jieming. shi @polyu edu. edu.hk
The Hong Kong Polytechnic University Hong Kong, China qing-prof 1il:pp1lll.....mmmm edu.hk
Abstract
Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence, limiting their reliability in critical applications. We propose SteerConf, novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability. SteerConf introduces three key components: (I) steering prompt strategy that guides LLMs to produce confidence scores in specified directions (e.g., conservative or opti- mistic) by leveraging prompts with varying steering levels; (2) steered confidence consistency measure that quantifies alignment across multiple steered confidences enhance calibration; and (3) steered confidence calibration method that aggre- gates confidence scores using consistency measures and applies linear quantization for answer selection. SteerConf operates without additional training fine-tuning, making it broadly applicable to existing LLMs. Experiments on seven bench- marks spanning professional knowledge, common sense, ethics, and reasoning tasks, using advanced LLM models (GPT-3.5, LLaMA 3, GPT-4), demonstrate that SteerConf significantly outperforms existing methods, often by significant margin. Our findings highlight the potential of steering the confidence of L.LMs to enhance their reliability for safer deployment in real-world applications. The implementation is at https: //gt thub. com/
Introduction
Large Language Models (LLMs) are widely used in applications such AI-driven chatbots
29
21 [3, medical diagnosis 30 24,, code generation 23 [5], legal document analysis 40
and education 34 [4]. Ensuring the reliability and trustworthiness of LLMs is critical, particularly in high-stakes domains like healthcare, legal analysis, and autonomous systems. However, LLMS often exhibit overconfidence, producing predictions that fail to align with their true likelihood of correctness, even when uncertain [38] posing significant challenges for their practical deployment.
To obtain prediction confidence from LLMs, confidence elicitation methods treat LLMs as black boxes, using prompts to elicit self-assessed verbalized confidence scores [38 (1] Unlike traditional approaches (22 (25) that rely white-box access to internal model information, some LLMs closed-source, with only commercial APIs, such as GPT-3.5 (28 and GPT-4 22), available for use.
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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These APIs do not provide access to internal details like token likelihoods, making traditional confi- dence elicitation infeasible. Additionally, fine-tuning LLMs for confidence elicitation prohibitively expensive, limiting its practicality for most researchers.
Recent studies focus on eliciting confidence from LLMs via prompts in black-box manner to improve confidence calibration, aligning confidence with accuracy and enhancing failure predic- tion-measuring the ability of LLMs to assign high confidence to correct predictions and low confidence to incorrect ones [38] Specifically, (38 propose a systematic framework that probes LLMs with various prompts, samples multiple responses elicit confidence scores, and aggregates them to obtain final confidence score. For instance, a Top-K prompting strategy [31 asks an LLM to provide the top-K most likely answers and their associated confidence scores for question. A sample prompt is: "Provide your K best guesses and the confidence that each is correct (0% to 100%) for the following question."
Observation. However, the prompt- ing strategies explored so far, do not explictly steer the direction of confidence in a controlled manner, i.e., whether the LLMs should be more conservative or optimistic in their confidence scores. Importantly. we find that if we explicitly instruct LLMs to be more conservative or optimistic, the LLMs can be steered to produce confidence scores in a specific direction. For example, we can steer LLMs to be very cautious answering questions and producing confidence scores, compared with the vanilla prompt, as shown in the example below.
997.0-
Wrong RESWIr
381.0-
Wriing arswor
Count
Canrecazseer
Count
Conrect aEower
498.5
190.5
0.0
40
80
100
60
100
Confidence (%)
Confidence (%)
(a) Vanilla Verbalized Confidence (b) Very-Cautious Steered Prompt
Figure I: Vanilla and very cautious Prompt Steered Confidence on Object Counting data with GPT-3.5.
Vanilla Prompt: "Read the question carefully, analyze step by step, and provide your answer along with your confidence. Confidence reflects how likely you believe your answer correct."
Very Cautious P Prompt: The Vanilla Prompt with an additional instruction: "You should adopt very cautious approach and assign low confidence to most of your answers."
Figure compares the effects of vanilla and very cautious steered prompts on the Object Counting Dataset 33 using GPT-3.5 (28). In Figure [ the vanilla prompt results in severe overconfidence, with LLMS assigning 100% confidence to answers that are only 50% accurate for counting tasks. In contrast, Figure lbb) shows that the very cautious steered prompt yields better-calibrated confidence scores, where confidence levels more closely align with actual accuracy. For example, answers with 100% confidence achieve approximately 90% accuracy, while those with 10% confidence correspond to about 10% accuracy. Quantitatively, the very cautious prompt improves calibration as measured by ECE and failure prediction as measured by AUROC, achieving a 29% increase in Area Under the Receiver Operating Characteristic Curve (AUROC, higher is better) and a 16% reduction in Expected Calibration Error (ECE, lower is better). These findings demonstrate the effectiveness of steering prompts in enhancing the reliability of LLM confidence scores.
Our Contributions. We propose SteerConf, novel framework that leverages the steerability of LLMs' confidence scores to enhance their reliability. SteerConf comprises three key components: (1) We propose a steering prompt strategy to guide LLMs in generating confidence scores in specified direction (e.g., conservative or optimistic) by employing prompts with varying steering intensities. For each question, we sample multiple responses, capturing both answers and their associated confidence scores across different steering levels. (2) We introduce a steered confidence consistency measure to evaluate the alignment of confidence scores across multiple steered responses, leveraging this consistency to enhance confidence calibration. (3) We design a stecred confidence calibration method that leverages the consistency measures to aggregate confidence scores and applies linear quantization to identify the most reliable answer based on the calibrated confidence. SteerConf is simple yet effective framework that can be directly applied to existing LLMs without requiring additional training or fine-tuning.
Comprehensive experiments across seven benchmarks, covering professional knowledge, common sense, ethics, and reasoning tasks, using three state-of-the-art models (GPT-3.5, I.LMM 3, GPT-4), demonstrate that SteerConf significantly outperforms vanilla verbalized confidence and existing

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calibration methods. For example, SteerConf achieves up to 39.8% reduction in ECE on GSM8K dataset with GPT-3.5 and 43.3% improvement in AUROC on the Object Counting dataset with GPT-4 in the CoT setting.
2
Preliminaries
2.1 Task Statement
Confidence represents a model's certainty in its predictions, typically expressed as a probability (e.g., "80% confidence"). score closer indicates high certainty, while a score near 0 reflects uncertainty. However, for large language models (LLMs), internal confidence scores are not directly accessible due to their black-box nature. Instead, confidence is elicited through verbalized responses obtained via prompting, where the model explicitly states its confidence in natural language (e.g.. "I am 80% confident answer A")
Verbalized Confidence.
31.
Verbalized Confidence Elicitation. This process involves modifying the original query prompt to explicitly request confidence scores from LLMs. For example, given the question "Which one is bigger, 9.8 or 9.10? Provide your answer," the prompt can be rephrased as "Which one is bigger, 9.8 or 9.1 Provide your answer along with your confidence (%)." The model might then respond with "9.8, 80%" instead of just "9.8," thereby verbalizing its confidence level as 80%.
Confidence Calibration and Failure Prediction. Verbalized confidence scores elicited from LLMs are often miscalibrated, meaning the reported confidence does not accurately reflect the true likelihood of correctness. For instance, model might state "80% confidence" for answers that correct only 50% of the time. Confidence calibration addresses this issue by aligning the model's confidence scores with the actual accuracy of predictions [T.. Ideally, predictions with 80% confidence should exhibit 80% accuracy. This is crucial for applications where confidence scores guide decision-making such in medical diagnosis or autonomous driving. The effectiveness of confidence calibration commonly evaluated using metrics such as ECE. Failure prediction focuses on identifying incorrect predictions by leveraging confidence scores. is formulated as a binary classification task, where correct predictions are labeled as O and incorrect ones as 1. The performance of failure prediction measured using AUROC, which assesses the model's 's ability to rank incorrect predictions below correct ones [ Detailed computations and evaluations are provided in the experiments.
2.2 Related Work
Elicitation of Confidence in LLMs. The concept of verbalized confidence, where models directly express confidence through natural language, was introduced in [22), but their work primarily focuses on fine-tuned models trained on specific datasets. Zero-shot verbalized confidence in LLMs remains unexplored in their study. Similarly, (25) trains external models to estimate confidence based on internal model representations, which are inaccessible in closed-source LLMs. While [44] examines the impact of confidence expressions, it does not provide explicit confidence scores to users. [3I] explores prompting strategies. proposing the Top-K verbalized prompting method. The unified framework summarized in [38), which considers sample consistency, is the most closely related to
our approach. contrast, our work introduces novel framework to steer LLM confidence in a desired direction, addressing gaps left by these prior studies.
Confidence Calibration. Modern Al systems often exhibit unreliable confidence estimates, fre- quently displaying overconfidence in their predictions 11 26 37 Calibration techniques address this issue by aligning confidence scores with actual accuracy rates 26). Two primary approaches have been explored: (1) scaling methods, which adjust confidence scores to improve reliability [1, 43), and (2) binning methods, which group predictions by confidence levels to assess and correct miscali- For LLMs, studies have revealed significant calibration challenges. For instance, tasks. Similarly, (2) found that while standard language models face calibration issues, pretraining can enhance confidence reliability. Conversely, [7 observed that larger LLMs, when properly prompted, exhibit improved calibration in multiple-choice tasks. However, a critical limitation of these stud- ies is their reliance on access to internal model computations (e.g., logits), which is infeasible for closed-soures systems like GPT-4. This limitation underscores the need for alternative confidence
bration 42 43
[66 demonstrated that models like T5 and GPT-2 are poorly calibrated in
3

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Hesser
Confelent
Vettalmed
Coutloos
Aumens Cortideezes
[r()]
Collect Confidences
Collent newers
Quepttoon (x) have tirne rabiits, 9lln, and truonlenee eew more adimols do have?
terd low soelldouse
Meun Confidence Standard Deviation of Conlidence
Auswer er Consesteacy Kour
9,
c(1) Me Rans Kiow/
Confidennce Consisteuyy
Callirated Confidence
76
Visilla
Final Oulpul
Koonf tadk
Selected A newer
④
Linser Quantization
Very Confidert
(1) Steering Prompts
(2) Stoored Confidence
Stocred Confidence Calibration
Consistency
Figure 2: Overview of the SteerConf framework, comprising three components:
Steering Prompting,
Steered Confidence Consistency, and Steered Confidence Calibration. Five steering levels, from very cautious very confident, elicit verbalized confidences and answers from the LLM. Confidence consistency K evaluates the stability of these confidences. The calibrated confidence c(x) computed by combining the mean confidence Mex confidence consistency Keonf, and answer consistency Kans. and the final answer fzx) is selected based on c(x).
estimation methods that operate without requiring internal model access modifications, motivating our proposed framework.
3 SteerConf: Steering the Confidence of LLMs
Figure illustrates the overall framework of SteerConf, comprising three key components: Steering Promptigg (Section 3.T. Steered Confidence Consistency (Section 3.2). and Steered Confidence Calibration (Section
Given an input question I, we first construct a set of steering prompts Pl. Po with varying steering levels (e.g., very cautious) to elicit steered answers ft(x) and confidence scores o (x) from the LLM. Next, the Steered Confidence Consistency module computes the confidence consistency Keon by analyzing the mean Ae and standard deviation of the sampled confidence scores q (z). The calibrated confidence score c(x) is then derived by combining Me. Kcon and the answer consistency Nana, which is determined by the relative frequency of the most frequent answer among f(()). Finally, the calibrated confidence score c(x) used select the final answer f(x) via linear quantization, mapping c(x) to the corresponding steering index in the confidence distribution.
3.1 Steering Prompts
As discussed in Section T. while LLMs often exhibit overconfidence, explicitly prompting them to be cautious can result in lower confidence scores. To leverage this behavior, we propose a steering prompting strategy systematically elicit and sample confidence scores from LLMs, enabling more accurate confidence estimation. Specifically, this strategy employs steering prompts that explicitly instruct the L.M adopt varying levels of caution confidence.
Specifically, we employ a set of steering levels to guide the LLM's confidence elicitation from very cautious to very confident: [very cautious, cautious, vanilla, confident, very confident); The steering level "very cautious" in the prompt instructs the LLM to be very cautious, with which the LLM tends to be more conservative in its confidence estimation. In the steering prompt, also include the original question to be answered, and the LLM is asked to provide both the answer and the confidence score. A brief example of very-cautious prompt is shown in the introduction. In the steering prompt, the steering level can be replaced with different levels, and the LLM asked to provide the answer and confidence score for each steering level. The detailed prompt design is shown in Appendix B
Formally, given an input text x, we can apply 28 + steering levels via steering prompts to the LLM, generating 2 + prediction-comfidende pairs. Let Pd be the (-th steering prompt, where

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I 1E(-t_-t-1....00 00.....-1,,f) Negative steering levels -t, -6+11,,, ................................ represent cautious steering levels, while positive steering levels 1, ..2..... represent confident steering levels. -6 and & represent the most cautious and most confident steering levels, respectively, while () represents the vanilla steering level. In other words, Poont is the most cautious steering prompt, P is the most confident steering prompt, and Pe is the vanilla steering prompt, which is the same with original verbalized confidence elicitation process.
In our setting, we set & = 2, which means we have five steering levels: (very cautious, cautious, vanilla, confident, very confident). a moderate granularity, which is sufficient to demonstrate the effectiveness of our method. Our framework is flexible and can be extended to more steering levels by increasing f. Given question 2; we construct the steering prompt set PPi Poonttl=- and for each steering prompt Poott we can elicit the answer and confidence score from the LLM get the prediction-comfidence pair (Ji(ar), (2)). where fi():) is the answer and cy (1)) is the confidence score. The confidence score et (x) is a real number between and I, representing the LLM's confidence
in its answer f1))). By applying different steering levels, we can probe the LLM's confidence in different directions and intensities, which can help us to better understand the LLM's confidence and uncertainty, to facilitate the accurate confidence estimation.
3.2 Steered Confidence Consistency
Given a set of sampled steered answers {f(( and their confidence scores {c(()))f__ we aim to compute a calibrated confidence score c(x) that reflects the model's certainty about answering the input question æ. In previous studies [38 6 5 20]. it has been shown that the consistency of multiple responses for a given question from L.Ms can provide meaningful guidance on model confidence. For example, the degree of agreement among multiple answers has been used serve as a measure of confidence for LLMs (38), which however overlooks the consistency of the verbalized confidences returned by LLMs, which may contain rich information of model's interior certainty.
Therefore, in this section, we propose a steered confidence consistency measure that considers the consistency of the steered verbalized confidence scores {c( to estimate the model's certainty about answering the input question x. The intuition that, even under different steering levels, if the LLM is able to respond with consistent confidence scores, it indicates that the model is confident about its answer, even with explicit prompt interventions. The higher the consistency of steered confidence scores, the more reliable the L.M is about its answer regarding the input question. To quantify this, we first compute the mean confidence Re of the steered confidences {cr (z) [=)]{==eee as in Eq. ①. representing the average certainty level, and the standard deviation o. in Eq. ② measuring how consistently this certainty is maintained across steering levels from -f to E. Intuitively, higher He with lower de indicates stronger confidence consistency.
Intuitively, the ratio Ss known as the coefficient of variation, naturally normalizes the dispersion of a distribution relative to the mean, showing the extent of variability in relation to the mean of the observations. A smaller value of de indicates that the data points tend be very close to the de mean and are more consitent, while larger value indicates that the data points are spread out over a wider range of values, being less consistent. Therefore, we propose the confidence consistency Neout Ito./po in Eq. which a bounded metric between 0 and 1, where Keoat = 1 indicates perfect consistency with ae = and Keonf = 0 indicates no consistency with infinite o..
Σ (())
Me =
28 +1 1--8
0e
1
Σ (c(z)) - pe)2
(2)
28 ++
Kiunf 1
(3)
5

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3.3 Steered Confidence Calibration
For question z, we aim to compute the calibrated confidence score c(x) that reflects the model's certainty about answering the question æ. In a nutshell, the calibrated confidence score c(x) should be high when the model is confident about its answer, and low when the model is uncertain about its answer. To achieve this, we propose to consider the mean confidence (ee confidence consistency Recoff and answer consistency Kians that is computed from the steered answers {f((r))
First, the answer consistency Kans is computed based on the relative frequency of the answers [38 For each answer y E {ff(=))f its frequency freq(y) is defined as the proportion of times y occurs among all steered answers: frre(()) = 2441 -i= (() y). where TI(.) is the indicator function. The answer consistency is then defined as the maximum relative frequency of the most frequent answer: Kans
freq(y). This reflects the degree of agreement among the answers, with higher Kans indicating greater consistency and reliability of the model's predictions.
Then, the calibrated confidence c(x: is defined as the product of the average steered confidence Pe- answer consistency Kans, and confidence consistency Reonf as in Eq. 4 The factor μec considers all the steered confidence scores, while Kans and Keonf, the two consistency metrics, are used to adjust the average steered confidence j4c based on LLMs self-consistency terms of answers and confidence scores. If the LLM shows low consistency in either answers confidence scores, the final calibrated confidence c(z) will be downweighted accordingly, and vise versa. This design ensures the robustness of the final calibrated confidence c(zz) against isolated nigh-confidence outliers that may arise from the black-box nature of L.LMs, as any single inconsistency component (Kans or Keont) can sufficiently downweight potentially miscalibrated
jlc-
<xx) = Kans
Ficonf
After obtaining the final calibrated confidence c(x) from Eq. 4 the step is select the final answer f(z) from the set of steered answers {fi(z)]{_- The selection should align with the calibrated confidence e(x), which represents the model's certainty.
Since c(z) may not directly match any of the steered confidence scores {<) we employ a mapping mechanism to determine the most appropriate answer. We map e(z) to the steering index space using linear quantization. First, compute the range of observed steered confidences:
Cmin min (x),
Cmax = max; ai (z).
(5)
Next, we map c(x) to a discrete steering index j using:
c(x)
Cmin
(2l + 1)
(6)
Cmax
Cmin
where [.] denotes the floor operation, ensuring j is an integer. Finally, the selected answer fxx) is determined as:
[s((()), ifj (z(1), if -
f(x)
This approach ensures that the answer f(x) is consistent with the calibrated confidence c(r), while maintaining numerical stability through explicit range normalization.
4
Experiments
4.1 Setup
Datasets. We assess confidence estimation quality across five categories of reasoning tasks: (1)
Commonsense Reasoning using Sports Understanding dataset (Sport) [18] and Strategy (Strat-
egyQA) 7 from BigBench B (2) Arithmetic Reasoning evaluated on GSM8K (GSM8K) 3
(3) Symbolic Reasoning covering Date Understanding (DateUnd) [36] and Object Counting (Ob-
jCnt) [33; (4) Professional Knowledge tested through Law (Law) from MMLU 12 ; and (5) Ethical
Knowledge examined via Business Ethics (Ethics) in MMLU [12].

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Model
GSM8K
DateU'd ObjCnt StralegyQA Sport
Eihies
Avg
Model
GSMBK DateUnnd ObjCnt
StralegyQA Sport
Law
Eliic
(Ag
LLaMA3
74.8
37.8
50.2
26.6
30.6
30,9
15.0
38,0
LLaMA3
53.7
50.3
50.3
58.8
51.5
51,0
42.3
[1.1
-SteerCont"
45.2
11.8
34.6
29.3
16.4
8.5
26.5
24.6
-SteerCont'
67.1
61.0
67.2
59.0
53.8
58.9
62.9
61.4
GPT-3.5
62.6
60.2
45.6
29.6
25.3
43.2
26.0
41.8
GPT-3.5
55.8
56.6
50.3
53.3
52.8
51.7
5488
53.6
"SteerCant'
22.8
33.0
27.5
24.0
10.8
2007
-StcerCont
82.9
60.5
82.5
58.6
606
71.0
66.2
GPT4
53.5
25.7
23.7
16.8
18.7
19.4
5.1
23.5
GPT-
52.0
50.5
50.4
55.6
57.9
56.6
84.1
[58.2
*SteerCont Conf
27.5
19.5
18.0
13.5
13.6
11.2
9.9
16.2
SSteerCooff
83.9
64.2
727
63.7
63.3
59.7
77.6
669.3
(a) ECE (Lower is Better)
(b) AUROC 1 (Higher is Better)
Model
GSMKK Datel nd OkjCint Strategy(A Sport
Law
Elhies
Avg
Model
GSMKK DateUnd ObjCint Strategy(A Sport
Law
Bliies
AAg
LLaMA3
81.7
38.2
50.2
33.0
34,8
40.8
30,3
144.
LLaMA3
21.3
62.5
49.9
77.2
66.9
61.0
71.5
58.6
SSeerCann
88.5
66.1
37.7
470
53.6
-SteerConn
28.1
68.0
62.3
79.6
66.2
79.1
64.11
GPT-3.5
76.9
66.0
16.1
37.6
32.3
51,8
37.5
55.2
GPT-3.5 SteerCoonf
30.0
41.5
54.5
66.2
70.8
47.5
69.6
[56.
SSeerConnf
93.5
64.1
76.4
43.5
41.7
64.4
51.7
62.2
60.4
54.4
83.3
71.1
76.1
52.3
80.2
68..1
GPT-
55.3
26.5
21.3
30.7
23.9
41.3
584
137.2
GPT-
47.3
71.5
76.5
77.6
82.6
67.5
91.5
(744
+SteerCont"
83.5
47.3
33.2
46.2
46.9
-SteerCanf"
76.8
81.2
81.4
71.3
92.0
81.6
(c) PR-N t (Higher is Better)
(d) PR-P 1 (Higher is Better)
Table 1: Comparing SteerConf with vanilla verbalized confidence elicitation of LLMs. ECE > 0.25,
AUROC, PR-P, PR-N < 0.6 denote significant deviation from ideal performance. The best is in bold.
Model
GSMBK DatUnnd OhjCnt StrategyQA Sport
Law
Edhics]
Avg
Model
GSMBK DateUnd OtjChi
StrategsQA Sport
Law
Eihies]
Avg
LLAMA3(aa))
5.0
13.7
8.7
11.8
7.7
22.8
7.8
11.11
LLMMA3(aT))
55.1
54.3
50.0
64.6
74.1
54.3
54.2
[58.
+SizerConf
7.8
1.0
6.5
8.6
5.4
20.9
5.0
7.9
SSieeroonf 81.2
70.7
87.6
74.5
79.4
64.7
81.2
77.0
20.3
30.8
41.8
26.0
20.5
44.3
24.8
29.8
GPE3SCCTT)
56.2
49.8
50.1
56.4
62.7
53.0
65.2
[56.2
SSperOonnf
65
67
19.7
14,5
10.8
16.0
15.1
12.7
+Steeroonf
85.6
76.0
82.5
67.5
66.4
61.5
86.7
75.2
GPT-4(aT)
6.5
18.5
922
6.1
10.7
GPT-4CaT)
75.1
50.0
68.8
87.6
665.5
SSteeConnf
56
7.2
SSterroonf
81.5
93.3
70.3
$1.1
(a) ECE (Lower is Better)
(b) AUROC f (Higher is Better)
Model
GSMWK Datelind ObjjCu StaatgyAAA Sport
Fahies
Avg
Model
GSMBK DueUnn ObyCm: Strategy
Sport
Law Ethics|
Avg
LLaMANCoT)
38.3
26.1
123.8
LLaMA/CoT) SSterCoonf
95.4
85.2
891
|65.
CStoorCont
41.3
24.0
49.0
43.2
47.5
50.5
51.7
43.9
97.8
92.6
98.1
90:0
92.8
95.1
91.1
(PCT)
26.5
28.0
42.0
36.8
46.9
55.5
45.7
140.2
((oo) 50.5
71.5
58.2
71.5
702
48.4
75.8
668.0
SSizerCont
70.9
49.7
14.7
48.9
52.
60.9
75.0
61.88
SSterCoonf
92.1
88.4
83.2
71.7
75.0
57.3
91.8
50.8
GPT-NCCUT)
10.5
47.6
4,9
48.7
27.7
46.2
83.2
4
GP GPT-
93.7
94.2
95.1
81.4
88.0
68.3
92.8
8816
*SteerCont
39.3
6188
44,9
38.9
48.7
35.4
74.9
54.9
+SteorConf
97.3
94.0
99.2
87.6
91.1
75.6
97.9
91.8
(c) PR-N 1 (Higher is Better)
(d) PR-P 1 (Higher is Better)
Table 2: Comparing SteerConf with verbalized confidence elicitation of LLMs with CoT. ECE > 0.25,
AUROC, PR-P, PR-N 0.6 denote significant deviation from ideal performance. The best is in bold.
Tasks and Metries. We evaluate our method SteerConf on two tasks: confidence calibration and failure prediction, following the protocol in [38. For confidence calibration, we report Expected Cal- ibration Error (ECE), which measures the average absolute difference between predicted confidence and empirical accuracy; lower ECE indicates better calibration. For failure prediction, Area Under the Receiver Operating Characteristic curve (AUROC), where higher values (closer to 1.0) indicate better discrimination between correct and incorrect predictions. To account for class (PR-P) and AUPRC-Negative
use the
imbalance due to varying accuracy, we further report AUPRC-Positee (PR-N), which respectively measure the model's ability to prioritize incorrect and correct predictions in precision-recall framework. Formal definitions of all metrics are provided in Appendix A
LLM Models. We evaluate several widely-used L.Mss: GPT-3.5 (28) I.LMA33 10) and GPT-4 29 LLaMA3 is used in its 70B parameter version with 4-bit quantization. Note that experiments with GPT-4 incurred a cost of approximately 1500 USD due to its higher pricing. We first compare
these LLMs using vanilla verbalized confidence elicitation, and then evaluate each LLM with CoT
prompting [55 for confidence elicitation. Results are reported in Table and Table 2 in Section 4.2
Details on the application of CoT are provided in Appendix B
Baselines. We also compare against sampling-based and prompting- based baselines summarized in [38), including Misleading [38], Self-Random (38), and Top-K 331, as reported Table All baselines use an LLM with CoT prompting as the backbone. Self-Random (38 leverages the model's inherent randomness by inputting the same prompt multiple times, and consistency aggregation
metric (38) is applied. Mislcading (38] evaluates the model's uncertainty by introducing misleading
7

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GSMBK
Law
DateUnd
StratogyQA
Ethies
Avg
Method
BCE
AUROC
BCE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
BCE
AUROC
GPT-3.5(CoT)
10.1
54.8
39.7
52.2
23.4
57.4
22.0
59.8
30.0
56.0
25.0
56.4
+Top-K
74.2
12.4
Miilenddnng
8.03
88.6
18.3
59.3
20.5
67.3
21.8
61.5
17.8
71.3
17.3
69.6
-Self-Random
6.28
92.7
26.0
65.6
17.0
66.8
23.3
60.8
20.7
79.0
18.7
+SteerConl (ours)
85.6
16.0
61.5
6.7
76.0
14.5
67.5
15.1
86.7
11.7
75.4
LLaMA3(CoT)
+Top-K
10.2
61.1
23.0
49.7
27.5
52.2
21.1
55.0
11.1
52.4
18.6
$4.1
Misleading
83.8
59.7
18.4
67.8
11.7
65.3
14.0
75.0
13.5
70.3
+Self Random
2.2
79.3
27.1
64.6
7.2
66.8
17.3
60.1
15.9
55.3
13.9
65.2
+SteerConf (ours)
81.2
3.3
64.7
10.0
70.7
4.3
74.5
13.6
81.2
74.5
Table 3: Comparison with baselines on GPT-3.5 and LLaMA3 with CoT. The best is in bold and the second best is underlined.
GSMBK
Law
DateUnd
StratogyQA
Ethics
Avg
Method
BCE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
BCE
AUROC
SteerConf Steering Prompting
70.2
69.7
Self-Random Consistency
2.2
79.3
27.1
64.6
7.2
66.8
17.3
60.1
15.9
55.3
13.9
65.2
SteerConf Consistency
25.9
68.2
17.2
14.3
68.5
66.7
Self-Random Avg-Conf
2.3
79.3
27.1
64.4
8.8
68.3
17.2
59.9
16.3
55.0
14.3
65.4
SteerConf Avg-Conf
71.3
25.8
63.3
67.1
174
61.3
16.7
73.5
14.3
67.3
SteerConf (ours)
81.2
3.3
64.7
10.0
70.7
4.3
74.5
13.6
81.2
7.5
74.5
Table 4: Component ablations and alternative aggregation schemes on LLaMA3with CoT. Lower ECE values indicate better calibration, while higher AUROC values reflect improved performance.
information and observing whether the model maintains its initial answer, inspired by human behavior
under confidence and consistency aggregation metric (38 is applied. Top-K [31 prompts the model
to generate answer-confidence pairs in a single response, pair-rank metric (38) is applied. For
Misleading and Selí-Random, we use M 5 samples; for Top-K, we K 5 answer-confidence pairs. We adopt GPT-3.5 and LLaMA3 both with COT.
4.2 Effectiveness Results
Comparison with LLMs with and without COT. Table and Table 2 report the performance of our method SteerConf versus vanilla verbalized confidence elicitation on LLaMA3, GPT-3.5, and GPT-4, without and with CoT prompting, respectively, across ECE, AUROC, PR-N, and PR-P metrics (all %). The last column in each table shows the average across datasets. Our method consistently outperforms vanilla verbalized confidence elicitation across nearly all metrics and datasets, both with and without COT, demonstrating improved confidence calibration and failure detection. For instance, in Table I a). GPT-3.5 with StecrConf achieves average ECE of 20.7%. substantially better than the vanilla ECE of 41.8% On ObjCnt, SteerConf attains an AUROC of 82.5%, ,, significantly better than 50.3% for vanilla GPT-3.5 (Table (b). CoT prompting further improves overall performance of all LLMs (Table yet SteerConf maintains a clear advantage. For example, in Table GPT)4(CoT) with SteerConf achieves average AUROC of 81.1%, versus 65.5% for vanilla. On the Ethics dataset, LLaMA3(CoT) with SteerConf yields PR-N of 51.7%, markedly higher than the vanilla baseline (Table 2 c)).
Comparison with Other Baselines. 3 presents a comparison of our method SteerConf with the Top-K, Misleading, and Self-Random baselines on LLaMA3 and GPT-3.5 with CoT. Across all datasets and metrics, SteerConf consistently outperforms the basclines, highlighting its superiority in both confidence calibration and failure detection. For instance, GPT-3.5(CoT), SteerConf achieves an average ECE of 11.7%, substantially lower than the 17.3% of the Misleading baseline. On LLaMA3(CoT), SteerConf attains average AUROC of 74.5%, outperforming all other baselines.
Ablation Study. We perform an ablation study to assess the impact of steering prompting and aggregation components, using LLaMA3 with CoT as the backbone. For the steering prompts component, removing it yields "SteerConf w/o Steering Prompting". For the aggregation component, we replace our proposed aggregation with Consistency and Avg-Conf, denoted as "SteerConf" + Consistency" and "SteerConf Avg-Conf". Ablating both components results in the Ranf-Random approach with Consistency Avg-Conf aggregation. The results reported in Table 4 which

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Self-Random
Misseading
Top-K
Coaf
ACEFS AB NOI 7 021322
CO0 ACC AAMOOC
ACESES OO
1230
Vrow
1061
Voog sore
906-
Vitag strve
695
Watep aone
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GSM8K
615
530
453
347
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Confidenee
(76)
Confidence (%)
Conffdance (%)
Confodence (%6
Acchaier 15
Com LATROAAI
CMT AATRDCAA
CMC AUCheci Aat.eaaAtooo Ner
318
141
296
119
Count
Drent
Frene
DateUnd
159
70-
148-
59
20
59
100
20
60
100
20
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100
Confiidnce (76
Confidence (%)
Confidenee (%)
Confidosse (%6
1981
Wrog More
Viogg sire Coreclaadd
1477
Wi soree Corve saRi
382
Wing Kcie
Count
QuatlgyQAA
191
20
20
20
Confidenne
Confidence
Confidenee
CORAAC 81.0 ALDOC 503) 10l 159
COBAaC Wid LAUAAO 150 ICE 140
COD sug AAAACCC 149
COC ACC upo AUBOCEL EXX
87
33-
59
31
Ethics
43
16-
29-
15
0
20
00
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80
100
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Coufidence
(75)
Coufidence (%)
Confiderce (%)
Confidarce (76)
1112
543
1013
438
Coart
Law
556
271
506-
219
20
20
100
20
100
20
Confidence (%)
Confiience (%)
Confidence (%)
Confidunce (%6
Figure 3: Confidence histograms of SteerConf and baselines for LLaMA3 with COT. The rows different datasets, and the columns are different methods.
demonstrates that each component contributes to improved performance, supporting the effectiveness of our method's design.
4.3 Empirical Confidence Distributions
Figure 3 presents the confidence histograms of SteerConf and baselines on LLLMA3 with COT. Each row corresponds to dataset, and each column shows the confidence distributions for Self Random, Misleading, Top-K, and our method SteerConf. The x-axis confidence, and the y-axis is the number of samples. Blue and red bars represent the confidence distributions for correct and incorrect answers, respectively. Compared to the baselines, SteerConf produces confidence distributions that are better separated between correct and incorrect answers, while baselines such Self-Randow tend to yield overconfident or less discriminative distributions. These results further validate the effectiveness of our method in improving confidence calibration, consistent with the quantitative findings in Section 4.2
4.4 Detailed Calibration Distributions
We provide the detailed calibration curves 32] of SteerConf and baselines on LLaMA3 with CoT. The results are presented in Figure 4 As depicted in Figure 4 the confidence distribution from vanilla verbalization is concentrated on a few discrete values. contrast, SteerConf yields a much more uniform distribution. Such continuous confidence values are more amenable to quantization, thereby improving model reliability, Moreover, we observe a better alignment between the calibrated confidence from SteerConf and the actual accuracy. On the Sport and StrategyQA datasets, for example, SteerConf shows consistent pattern of alignment across the full spectrum of confidence values, demonstrating the effectiveness of our approach.

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Confifoncee Histognm
Contidonce Histogna
Conckfence Histogram
Concilence Histogram
1444
Ang. Nottuu
Arg. Notttu
1.0
Fre Accticy
Arg Auieey
Reieeun Gemple
Reienu Sengles
5.0
0.2
0.2
300
0.2
1.0
022
Rellability Diagram
Reliability Diagram
Reliabilitty Diagram
Rellabilig by Diegram
0.75
0.75
Ray
Hay
0,50
0.52
0,50
6,90
Ondidenoc
Onridenss
Carflless
(a) Law (vanilla)
(b) Sport (vanilla)
(c) ObjCnt (vanilla)
(d) Strategy QA (vanilla)
Confifonne Histogna
Confidence Histogram
Concidence Histogram
ConciMence Histogram
Ang. Nucsouu
Anp Aucesu
0.75
Are Auceesc
Arp Auceacy
Auddin St
Audais
50
0.2
12
0.2
0.2
1.0
0.2
Checidenod
Chntidence
Confidencs
Coriidencs
Relisbility Dingram
Reliability Dingram
Relishility iyy Diggom
Relishil by Dagrm
0.75
0.75
0.75
Ray
Ray
0.50
0,50
0.53
0,90
0.25
0.25
0.0
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Contdracs
Contdrace
Chntdraac
Crrtdeace
(c) Law (SteerConf)
(f) Sport (SteerConf)
g ObjCnt (SteerConf)
(h) Cont)
StrategyQA
(Steer-
Figure 4: Expected Calibration Error (ECE) plots and reliable diagrams for LLaMA3-70B model. The columns correspond to four tasks: Law, Sport, ObjCnt, and StrategyQA. The top row shows results of vanilla verbalized confidence, while the bottom row shows results from our SteerConf. In each plot, the upper part shows the ECE plots, and the lower part shows the reliable diagrams.
Conclusion, Limitations, and Future Work
In this paper, we introduced SteerConf, framework for improving LLM confidence reliability through semantic steering. Our approach addresses the critical issue of overconfidence, which undermines trustworthiness in high-stakes applications. By leveraging carefully designed prompts, we systematically steer LLM confidence in desired dircctions. The framework comprises three key components: steering prompt strategy, steered confidence consistency measure, and steered confidence calibration method. Together, these components enhance confidence calibration without requiring model internals fine-tuning. Comprehensive evaluations across seven benchmarks with state-of-the-ae LLMs demonstrate consistent improvements over existing methods. This work has significant implications for deploying LLMs in critical domains where reliable confidence estimation is essential. By offering an effective black-box approach, SteerConf enables trustworthy AI without requiring specialized expertise or access model internals.
Limitations and Future Work. It is common to mannually design prompts for LLMs, and our method is no exception. Future work will explore automatic prompt generation to address this. Additionally, using multiple prompts increases computational costs; we aim to develop adaptive strategies to optimize prompt usage based on task and model needs. Following prior work, our current tasks focus on numerical answers (GSM8K) or multiple-chor choice questions (Law), where ambiguity is minimal and answer consistency is both sufficient and effective. Extending to more open-ended tasks is an important future direction.
Broader Impact. This work improves LLM confidence elicitation, enhancing their reliability and trustworthiness in high-stakes applications. While we foresee no direct negative societal impacts, LLM misuse remains concern. We advocate for responsible Al practices to mitigate such risks.
10

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Acknowledgments
This work supported by grants from the Research Grants Council of Hong Kong Special Admin- istrative Region, China (No. PolyU 25201221, 1, No. PolyU 15205224), NSFC No. 62202404, and Smart Cities Research Institute (SCRI) (001003-P005064)
References
[1] Kendrick Boyd, Kevin H Eng, and C David Page. Area under the precision-recall curve: point estimates and confidence intervals. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III 13, pages 451 551-466. Springer, 2013.
[2] Yangyi Chen, Lifan Yuan, Ganqu Cui, Zhiyuan Liu, and Heng A close look into the calibration of pre-trained language models. ACL, 2023.
[3] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers solve math word problems. CoRR, abs/21 10.11688, 2021.
[4] Ailin Deng, Miao Xiong, and Bryan Hooi. Great models think alike: Improving model reliability via inter-model latent agreement. In ICML, 2023.
[5] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, 2016.
[6] Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muham- mad Shahzad, Wen Yang, Richard Bamler, and Xiaoxiang Zhu. A survey of uncertainty in deep neural networks. Artif. Intell. Rev., 2023.
[7] Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. Did aristotle use a laptop? A question answering benchmark with implicit reasoning strategies. Trans. Assoc. Comput. Linguistics, 2021.
[8] Ahmad Ghazal, Tilmann Rabl, Minqing Hu, Francois Raab, Meikel Poess, Alain Crolotte, and Hans-Amo Jacobsen. Bigbench: Towards an industry standard benchmark for big data analytics. In SIGMOD, 2013.
[9] Ethan Goh, Robert Gallo, Jason Hom, Eric Strong, Yingjie Weng, Hannah Kerman, Joséphine A Cool, Zahir Kanjee, Andrew S Parsons, Neera Ahuja, al. Large language model influence on diagnostic reasoning: randomized clinical trial. JAMA Network Open, 2024.
[10] Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint 2rXiv:2407.21788. 2024.
[11] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. In /CML, 2017.
[12] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In ICLR, 2021.
[13] Christian Hildebrand and Anouk Bergner. Ai-driven sales automation: Using chatbots boost sales. NIM Marketing Intelligence Review, 2019.
[14] Stefan E Huber, Kristian Kiili, Steve Nebel, Richard M Ryan, Michael Sailer, and Manuel Ninaus. Leveraging the potential of large language models in education through playful and game-based learning. Educational Psychology Review, 2024.
[15] Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim. survey on large language models for code generation. arXiv preprint arXiv:2406.0005/ 2024.

---

[16] Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. How can we know when language models know? on the calibration of language models for question answering. TACL, 2021.
[17] Sauray Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson et al. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221, 2022.
[18]
Ethan Kim. Sports understanding in bigbench, 2021.
[19] Jinqi Lai, Wensheng Gan, Jiayang Wu, Zhenlian Qi. and Philip S Yu. Large language models in law: A survey. Al Open, 2024.
[20] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. NeurIPS, 2017.
[21] Chien-Chang Lin, Anna YQ Huang. and Stephen JH Yang. A review of ai-driven conversational chatbots implementation methodologies and challenges (1999-2022). Sustainability, 2023.
[22] Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words. TMLR, 2022.
[23] Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. NeurIPS, 2023.
[24] Xiangbin Meng. Xiangyu Yan, Kuo Zhang, Da Liu, Xiaojuan Cui, Yaodong Yang, Muhan Zhang, Chunxia Cao, Jingjia Wang, Xuliang Wang, et al. The application of large language models in medicine: A scoping review. Iscience, 2024.
[25] Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. Reducing conversational agents' overconfidence through linguistic calibration. TACL, 2022.
[26] Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic. Revisiting the calibration of modern neural networks. In NeurlPS, 2021.
[27] Mahdi Pakdaman Nacini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In AAAI, 2015.
[28] OpenAI. ChatGPT.
2021.
[29] OpenAl. GPT-4 technical report. CoRR, abs/2303.08774, 2023.
[30] Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature medicine, 2023.
[31] Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. In EMNLP, 2023,
[32] Ben Van Calster, Daan Nieboer, Yvonne Vergouwe, Bavo De Cock, Michael J Pencina, and Ewout W Steyerberg. A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of clinical epidemiology, 2016.
[33] Jianfeng Wang, Rong Xiao, Yandong Guo, and Lei Zhang. Learning to count objects with few exemplar annotations. arXiv preprint arXiv: arXiv:1905.00 190500789, 2019.
[34] Yongliang Wang and Lina Xue. Using ai-driven chatbots foster chinese efl students' academic engagement: An intervention study. Computers in Human Behavior, 2024.
[35] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. NeurIPS, 2022.
12

---

[36] Xinyi Wu and Zijian Wang. Data understanding in bigbench, 2021.
[37] Miao Xiong, Ailin Deng, Pang Wei W Koh, Jiaying Wu, Shen Li, Jianqing Xu, and Bryan Hooi. Proximity-informed calibration for deep neural networks. NeurIPS, 2023.
[38] Miao Xiong. Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.
In ICLR,, 2023.
[39] Miao Xiong, Shen Li, Wenjie Feng, Ailin Deng, Jihai Zhang, and Bryan Hooi. Birds of a feather trust together: Knowing when trust classifier via adaptive neighborhood aggregation. TMLR, 2022.
[40] Xiaoxian Yang, Zhifeng Wang, Qi Wang, Ke Wei, Kaiqi Zhang, and Jiangang Shi. Large language models for automated q&a involving legal documents: survey on algorithms, frameworks and applications. International Journal of Web Information Systems, 2024.
[41] Zhuoning Yuan, Yan Yan, Milan Sonka, and Tianbao Yang. Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification. In ICCV, 2021.
[42] Bianca Zadrozny and Charles Elkan. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In ICML, 2001.
[43] Jize Zhang, Bhavya Kailkhura, and T Yong-Jin Han. Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning. ICML, 2020.
[44] Kaitlyn Zhou, Dan Jurafsky, and Tatsunori B Hashimoto. Navigating the grey area: How expressions of uncertainty and overconfidence affect language models. In EMNLP, 2023.
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A Task Statement And Metric
Confidence Calibration. LLMs often exhibit misaligned confidence scores, frequently overestimat- ing the reliability of their predictions. The goal of confidence calibration is to align these confidence scores with the actual accuracy of the predictions. To evaluate calibration performance, we use the Expected Calibration Error (ECE) [27 [9] as the primary metric.
Given set of imput samples {-r)== with corresponding labels {us LLM predictions {f( and confidence scores {c(2,))e= we partition the samples into B bins based on ( (.). The Expected Calibration Error (ECE) is their confidence scores: 2; E BB if c(x;) E B computed as:
ECE = ΣE [al |acc Bb) comf(B;))]
(7)
where B₁ the set of samples in the b-th bin, [Bol the number of samples the b-th bin, ace( Bb) Bal Ex,BB, ((f(2))(()))))))))))))))))())1 ys) is the accuracy of the samples in the b-th bin, and conf Bb)
B. EZ,BB, c(z;) is the average confidence score of the samples in the b-th bin. Here, II(.) is the indicator function, which returns if its argument is true, and O otherwise.
Failure Prediction. Failure prediction involves determining whether an LLM's prediction correct based on its confidence score. This task is evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) M. Given set of input samples {ara J.W the task is framed binary classification problem, where the input is the confidence score c(z;). and the label indicates the correctness of the LLM's prediction, (x;) y1)) The AUC-ROC is defined as:
AUC-ROC So TPR(() dFPR((),
(8)
where Tt)(t) (True Positive Rate) and FPR(t) (False Positive Rate) at threshold are computed as:
N
(PR(() =
Σ
I(c(a,) 2 ()(((((ss)))))) = y:),
(9)
N
FPR(()
[(c(x;) - I(f(xs) wi))
(10)
N
Precision-Recall Perspective. To complement AUC-ROC, we evaluate failure prediction using the Area Under the Precision- Recall Curve for the positive class (PR-P) and the negative class (PR-N). These metrics are particularly informative under class imbalance. For PR-P, the positive class corresponds to correct predictions (f(zz) y:). while for PR-N. the positive class corresponds to incorrect predictions (f(;;) * y:). The Precision-Receee Curve (PRC) is parameterized by threshold t, with precision and recall defined as:
N EE I(c(x)) 2 ();()();)) 91)
Precision,
(11)
Σe I(((x;) 2t)
Recall, Li=1 T(((x:) 2 (f; vi) ΣN 1(f(x,)) =y))
(12)
PR-P, and analogously for PR-N by replacing I([(x;) = y:) with I(f(x;; + ys) in both the numerator and denominator. The AUPRC values are computed as:
PR-P = So Precision,,)) (t) PR-N So Precision, (t) dRecall, (t).
(13)
PR-P emphasizes the model's 's ability to identify correct predictions across confidence thresholds, while PR-N focuses on detecting failures. Together, these metrics provide a nuanced evaluation of calibration quality, particularly under skewed class distributions.
B Steering Prompts Used
In this section, we present the detailed prompts used our experiments for L.LMs under both CoT and non-CoT settings. The steering levels (very cautious, cautious, vanilla, confident, very confident
21

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} are described as follows. Section B.1 provides the prompts for the CoT setting, while Section B.2 covers the prompts for the non-CoT setting.
Discussion on Instruction- Followingg Ability. Instruction-followinng is prerequisite. Verbalized confidence assumes the model can return answer-confidence pairs in fixed format. In practice, smaller or less capable models may fail at this step, which we treat as a formatting failure rather than a limitation of steering. Once model reliably produces answer-confidence pairs, we find also follows our steering prompts (e.g., very cautions vs. very confident) and shifts its confidence accordingly (see Figure 5
for how the model's confidence changes with different steering levels).
Discussion on Additional Query Cost. We deliberately query the model multiple times under a small set of steering prompts. In our black-box setting, we cannot read internal signals (c.g., logits), so these extra queries give us the missing evidence about how stable both the answers and the confidence scores are. This is test-time scaling trade-off:: spend a few more forward passes obtain much better-calibrated confidence and more reliable failure detection, without any training or access to internals. For complex, multi-turn reasoning, the same idea applies-investing handful of additional queries typically yields more dependable confidence estimates and thus more trustworthy traces.
B.1 With CoT
very cautious. Read the question, analyze step by step, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a wrong answer with high confidence. (3) You should be very cautious, and tend to give low confidence on almost all of the answers. \nUse the following format to answer:\** answer\""Explanatioo" Explanation: [insert step-by-step analysis here Bere]lnAnsswe and Confidence (0-100): [ONLY the (ANSWER_ TYPE); not complete sentence], [Your confidence level. please only include the numerical number in the range of 0-100]% "Annnlly give me the reply according to this format, don't 't give me any other words.
cautious. Read the question, analyze step by step. provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a wrong answer with high confidence. AnUse the following format
[insert step-by-step analysis here]|nAnswer and Confidence (0-100): [ONLY the {ANSWER :YPE): not a complete sentence), [Your confidence level, please only include the numerical number in the range of 00100]%""'nnnn give me the reply according to this format, don't 't give me any other words.
vanilla. Read the question, analyze step by step, provide your answer and your confidence in this answer. Note: The confidence indicates how likely you think your answer is true. AnUe the following format to Inswerr'""Exllanattio Explanation: [insert step-bg sep-by-step analysis here]InAnswer and Confidence (0- 100): [ONLY the [ANSWER_ _TPE]; not a complete sentence], [Your confidence level, please only include the numerical number in the range of 0- 11100000n"""nnO give me the reply according to this format, don't t give me any other words.
confident. Read the question, analyze step by step. provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a right answer with low confidence. AnUse the following format to answer:\" " EExplanation: [insert step-by-step analysis here]\nAAswer and Confidence (0- (00):)): [ONLY the (ANSWER TYPE); not complete sentence], |Your confidence level, please only include the numerical number in the range of 0- "nnnly give me the reply according to this format, don't I give mc any other words.
very confident. Read the question, analyze step by step, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a right answer with low confidence. (3) You should be very confident, and tend to give high confidence on almost all of the answers. InUse the following format to answer:\ "Exp:antEExaaanatioon [insert step-by-step analysis here]\nAnswer and Confidence (0-100); [ONLY the [ANSWER _TYP): not complete sentence], |Your confidence level, please only include the numerical number in the range of 0- (0nnly give me the reply according to this format, don't I give me any other words.
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B.2 Without CoT
very cautious. Read the question, provide your answer and your confidence this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a wrong answer with high confidence. (3) You should be very cautious, and tend to give low confidence on almost all of the answers. AnUse the following format to answer:'n" "n'serr'n""Answer "nnswr and Confidence (0-100): [ONLY the ANSWER SEPE, not a complete sentence], [Your confidence level, please only include the numerical number in the range of 0- 10000%""Annnll the answer and confidence, don't give me the explanation.
cautious. Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving wrong answer with high confidence, \nUse the following format to answer'in" " "nnwwr and Confidence (0-100) [ONLY the ANSWER_TYPE complete sentence), [Your confidence level, please only include the numerical number in the range of 0- 0-100]%""'nOnl) the answer and confidence, don't give me the explanation.
not a
vanilla. Read the question, provide your answer and your confidence in this answer. Note: The confidence indicates how likely you think your answer is true.AnUse the following format an- swer:n' Answer and Confidence (0-100); [ONLY the ANSWER_TW [Your confidence level, please only include the numerical number in the range of 0- 0-100j%""nnnnll the answer and confidence, don't give me the explanation.
not complete sentence],
confident. Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving right answer with low SoffidenneeAnUSS. the following format to answer:In" Ansser and Confidence (0-100): [ONLY the DESWER_YPPE not a complete sentence], [Your confidence level, please only include the numerical number in the range of 0- O-100]%%"\nOnly the answer and confidence, don't It give me the explanation.
very confident. Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving right answer with low confidence. (3) You should be very confident, and tend to give high confidence on almost all of the answers. \nUse the following format to answer:An AAnseer complete sentence], [Your confidence level, please only include the numerical number in the range of 0- "lnnly"nnnnny the answer and confidence, don't 't give the explanation.
Answer and Confidence (0-100): [ONLY the ANSWER_TYY __PP; not
C Effects of Steering
To further understand the effects of steering on confidence, we present the confidence histograms of SteerConf with different steering levels from very cautious to very confident on LLaMA3 with CoT on DateUnd, GSM8K, Ethics, StrategyQA and Law datasets in Figure 5 Observe that the confidence distributions of SteerConf gradually change with varied steering levels. For example, on the StrategyQA dataset, the confidence distribution of very cautious more conservative than that of very confident. This demonstrates the effectiveness of the proposed prompt steering strategy in SteerConf.
[ablecS the effects of steering by presenting the differences in various metrics between the steered confidence distribution and the vanilla verbalized confidence distribution for GPT-3.5 without COT. The steering effects are pronounced for high-intensity steering levels (very cautious, very confident) across most datasets, aligning with the intended directions. For instance, in the GSM8K dataset, the JS Div metric for very cautious is reduced by 52.11% compared to vanilla, while for very confident, increases by 23.72%. However, for moderate-intensity levels such as confident, the steering direction may deviate from expectations. For example, in the Ethics dataset, the Mean metric for confudent 1.79 lower than vanilla. This highlights the importance of carefully designing the intensity of steering to achieve the desired effects.
23

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COP-ACE BEAUBOO 9O17 ICE
EXT
ACCIAT STATBOO IICE
ACCACT AL BOC PC
DOEAAY SUT AEROC 142
DOTACY STADWOC
129
338-
346-
352
346
Couutt
Chared
Cornct
64
169
173
176
173
0
0
60 RD 100
40
N0
80
100
20 40
80
80 100
20
40
50
RD
100
0 20
40
80
100
ConÉdence (%6
Confelence (%6)
(%) Vanilia
Confidange ( Confident
Confidency 1%)
Very catmous
Cautious
Very confiidet
(a) DnteUnd
CODAAC M4/AABOC (25) 2
COC SAS AUBOC 55.7 BET
OCTACC MNP/A300C 1/
COLAACIS AUROC'SS ICEAS
DUT ACCLACOSSH ACROC 523/ D02
7.99
1303
1306
1307
mi
1311
Nasgeame
Count
384
651-
653
653
655
0-
Confidence
Confidence (%6)
Confidence (%)
Confidance (%)
Confidance (%) Very confident
Very caulious
Cautious
Vanilla
Contident
(b) GSM8K
Weig mevi
Mingg
45
Aaag Consorevnt
canur Cornet srvee
Count
Conte exeto
LEINT
28
22
16
20
40
60
RD
100
0
20
80
100
20
60
80
100
40
60
100
20
40
100
Confidence
(%)
Confidence (16)
Confidoage (t))
Confidence (%)
Confidencs 1%)
cry cruthous
Cautious
Confident
Very confident
(c) Ethics
CODAAC TIO/ABBOC dhu 20E
CUC ACO TI ARAA/ //DCE DAR
COTL ACC The AUBOU DIE
OC AUROC ALL IIT/
OCTAAC STADRO AEA 20E
850-
792
919
959
955
Count
425
396-
459
479
477
0
0
100
60
100
20 40
60
100
20
20 40
Confedence
Cantidency Very contiie
Very calitiuss
Cintious
Vanilla
Conlident
(d) StrategyQA
Con 4L.
DOS
776-
848
659
S83
Strne mover
Count
388
424
291
20
60
80 100
60 80 100
20 40
50
80 100
20 40
50
20 40
100
Confidence (%6)
Confidegco (%)
Confidrace (%)
Confidurce (%6
Confidmce (%) Very confident
Very cantious
(c) Law
Figure 5: Confidence histograms for LLaMA3 with CoT on DateUnd, GSM8K, Ethics, StrategyQA and Law datasets. The columns are different steering levels.
D Per-Prompt Ablation
To assess the contribution of each steering prompt, we perform a per-prompt ablation using LLaMA3 with CoT as the backbone model. For example, "SteerConf w/o very confident removes the very confident prompt. Results are reported in Table 6 We observe that cautious prompts (cautious, very cautious) chiefly improve AUROC, whereas confident prompts (confident, very confident) primarily reduce ECE.
E Results with a Smaller Model
We conduct experiments using the small model Qwen3-1.7b with the CoT setting, as shown in Table g Due to the model's 's relatively weaker instruction instruction-follovingg ability, frequent collapsed verbalized confidence elicitation renders results on some datasets (Law and Ethics) unusable. Therefore, we report results only on datasets with reliable responses. The results demonstrate that our proposed method consistently outperforms the vanilla verbalized confidence approach with the CoT setting, even with small model backbone.
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Dataset
Was Dis
JS Div
Mean
Dataset
Was Dis
JS Div
Mcan
Sport
13.83
-41.74
-14.66
Sport
-0.27
-4.18
-0.83
Fthics
-21.20
-72.34
-20.49
Ethics
-1.40
10.63
-0.04
Law
-18.49
-59.99
-17.74
Law
-1.11
-8.13
-1.59
ObjCnt
-11.42
-34.23
-16.93
ObjCnt
-0.01
-1.86
-0.13
StrutegyQA
-15.14
46.66
-16.94
StrategyQA
-0.28
-3.47
-0,99
DateUnd
-19.27
-60.82
-25.33
DateUnd
-0.08
-3.19
-1.22
GSM8K
-24.77
-52.11
-25.98
GSM8K
-0.50
-3.34
-1.02
(a) very cantious v.s. vanilla
(b) cautious v.S. vanilla
Dataset
Was Dis
JS Div
Mean
Dataset
Was Dis
Div
Mean
Sport
-0.53
-4.93
-0.47
Sport
1.11
17.43
1.71
Bthics
-1.20
-00.08
-1.79
Ethics
1.30
14.31
2.05
-0.79
-6.45
-1.33
Law
1.73
19.22
3.26
ObjCnt
-0.05
-2.79
-0.09
ObjCnt
0.08
2.63
0.08
StrategyQA
-0.46
-5.52
-1.02
StrategyQA
0.97
13.16
1.41
DateUnd
-0.19
-4.84
-1.44
DateUnd
-0.19
-4.75
-0.69
GSMBK
-0.85
-4.41
-1.58
GSM8K
4.08
23.72
5.36
(c) confident v.s. vanilla
(d) very confident v.s. vanilla
Table 5: Effects of Prompt Steering measured by different metrics GPT-3.5. The confidence distribution is treated as a discrete distribution over the interval [0, 1,00%, divided into bins of length 5. Each sub-table (a)-(d) shows the difference in distributions between specific steering level (e.g., very cautious) and the vanilla verbalized confidence elicitation. "Mean" represents the average confidence for the dataset. "Was Dis" refers the Wasserstein Distance, and "JS Div" denotes the Jensen-Shannon Divergence. To indicate the steering direction, the sign of the "Mean" metric is applied to these non-negative distribution metries.
Method
GSMBK
Law
DateUnd
StrarogyQA
Ethics
Avg
ECE
AUROC
ECE
AUROC
BCE
AUROC
ECE
AUROC
BCE
AUROC
ECE
AUROC
SteerConf w/o confident
7.2
80.9
5.1
64.3
11.1
70.9
5.8
74.3
17.9
80.4
9.4
74.1
SteerConf w/o confident
7.4
80.8
5.3
64.6
11.1
74.3
16.0
81.4
9.1
74.2
SteerCont w/o vanilla
6.8
79.3
4.4
64.2
11.4
68.5
74.4
12.9
78.7
8.3
73.0
SteerConf w/o caatiour
6.9
80.7
4.7
63.8
9.3
68.3
74.2
12.9
77.2
7.8
72.8
SteerCont C'onf w/o very cantious
3.1
78.0
13.3
65.7
11.3
69.9
72.4
6.3
78.0
8.3
72.8
SteerConf
6.1
81.2
3.3
64.7
10.0
70.7
74.5
13.6
81.2
7.5
74.5
Table Per-prompt ablations on LLaMA3 (CoT) by removing one steering prompt at time. Lower ECE values and higher AUROC values indicate better performance.
F
Additional Comparison with Avg-Conf Baselines
Table 9 compares SteerConf with more aggregation schemes using the LLaMA3 backbone with CoT setting. The results show that our proposed method SteerConf outperforms all baselines, including Self- Self-Random and Misleading variants with Consistency, Pair-Rank and Avg-Conf aggregation. For instance, SteerConf achieves ECE of 7.5%, notably better than Self-Random with Avg-Conf that is 14.3%.
G Prompt Robustness: SteerConf (brief) vs. Original SteerConf
We provide experiments to demonstrate the robustness of our method to prompt variations. We modified the expression of the steering prompts to be more concise while preserving the core steering intent. For example, simplified the "very cautious prompt follows:
Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a wrong answer with high confidence. (3) You should be very cautious, and tend give low confidence almost all of the answers.
Original SteerConf:
25

---

Method
GSM8K
Law
DateUnd
StrategyQA
Ethics
Avg
BCE
AUROC
RCB
AUROC
ECR
AUROC
BCE
AUROC
BCE
AUROC
BCB
AUROC
GPT-3.5
62.6
55.8
43.2
51.7
60.2
56.6
29.6
53.3
26.0
54.8
44.3
54.4
+SteerConf (brief)
9.6
82.4
9.3
61.3
12.1
55.0
21.0
58.4
14,8
68.1
13.3
65.0
+SteerConf
22.8
82.9
24.0
33.0
60.5
14,9
71.0
21.1
66.7
Table Prompt robustness on GPT-3.5: SteerConf (brief) vs. original steering prompts SteerConf. Lower ECE and higher AUROC are better.
Method
GSM8K
Sport
DateUInd
StrategyQA ECE
OhjyCnt
Avg
ECE
AUROC
ECE
AUROC
ECE
AUROC
AUROC
ECE
AUROC
|CC
AUROC
Qwen3-1.7h
25.5
54.7
37.2
45.5
62.4
37.2
55.5
32.4
65.3
35.5
58.8
Qwen3 1.7h SteerConf
8.3
80.4
40.1
49.8
46.5
62.3
39.2
51.1
22.0
74.5
31.2
63.6
Table 8: Results on Qwen3-1.7b with CoT prompting. Lower ECE and higher AUROC are better.
SteerConf (brief): Read the question, provide your answer and your confidence in this answer. Note: (I) The confidence indicates how likely you think your answer will be true. (2) Be very cautious, tend to give very low confidence on every answer.
We compare the modified and original SteerConf prompts using GPT-3.5 as the backbone, as shown in Table 7 Notably, SteerConf (brief) achieves lower ECE than SteerConf, and both outperform GPT-3.5. These results demonstrate the robustness of our method to prompt variations.
H Per Steering Prompt Performance
In Table IO we report results using only the very cautious, cautious, confident, and very confident prompts in the LLaMA3 with CoT setting, respectively. SteerConf consistently achieves the best overall performance in terms of average ECE and AUROC, though a single prompt could outperform it specific metrics. This result validates the effectiveness of our aggregation component on these single steering prompts.
I Effect of Steering Levels (e (f= vs. l= =
To assess the trade-off' between performance and query cost, we vary the number of steering levels from 5 to 3 to evaluate their impact, using LLaMA3 with COT.
Table 11 reports results on LLaMA3 with CoT, where 'nn_vvrry' omits the most extreme prompts (very cautious and very confident) and "no_ "old" omits the mild ones (cautious and confident).
J Discussion on Answer Selection and Effects on Accuracy
In this section, we analyze the impact of the answer selection mechanism the SteerConf method. We denote SteerConf (Majority) as a variant of SteerConf, where the answer selection is replaced with a majority voting mechanism [38). Specifically, in SteerConf (Majority), the final answer is determined by the most frequent response. In cases of ties, the answer with the highest mean steered confidence score among the tied responses is selected. The results in Table [22 demonstrate the calibration effectiveness of our approach. For instance, with ILLMA33, the average ECE for SteerConf 7.3%, compared to 7.8% for SteerConf (Majority). Similarly, for GPT-3.5, the average PR-N metric for SteerConf is 62.0%, significantly outperforming SteerConf (Majority) at 53.9%. Table 12 also highlights the trade-off between calibration and accuracy. While SteerConf achieves superior calibration compared to vanilla verbalized confidence elicitation, it incurs a slight reduction in accuracy. Conversely, SteerConf (Majority) offers marginally better accuracy than SteerConf but at the expense of slightly weaker calibration. For example, with LLaMA3, the average accuracy of SteerConf is 82.3%. compared to 83.6% for SteerConf (Majority). while vanilla LLaMA3 achieves 83.1% 1%. In summary, SteerConffi is recommended for scenarios prioritizing calibration, while SteerConf (Majority) is better suited for applications where accuracy is more critical.
26

---

GSMSK
Law
DateUnd
StrategyQA
Ethics
Avg
Method
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
Vanilla
5.0
55.1
13.7
54.3
11.8
54.2
12.2
56.5
Mislending Consistency
4.4
83.8
18.9
39.7
18.4
67.8
11.7
65.3
14.0
75.0
13.5
70.3
Self-Random Consistency
2.2
79.3
27.1
64.6
7.2
66.8
17.3
60.1
55.3
13.9
65.2
Misleading Avg-Cont Conf
3.3
83.5
18.8
62.4
15.6
70.4
11.1
67.8
14.9
75.2
12.8
71.9
Self-Random Avg-Conf
2.3
79.3
27.1
64.4
8,8
68.3
17.2
59.9
55.0
14.3
65.4
Top- K Bank
10.2
61.1
23.0
49.7
27.5
52.2
21.1
55.0
11.1
52.4
18.6
54.1
Top K. + Avg. Conf
56.4
60.6
15.1
55.8
40.7
55.9
36.4
52.0
5.2
67.5
30.7
58.4
SteerCond (ours)
6.1
81.2
3.3
64.7
10.0
70.7
4.3
74.5
13.6
81.2
7,5
74.5
Table 9: Comparison with additional aggregation schemes LLaMA3 with CoT, including Avg- Conf and Pair-Rank/Top-K Lower ECE and higher AUROC are better.
GSMBK
Law
DateUnd
StrategyQA
Ethics
Avg
Method
ECE
AUROC
ECB
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
very
9.9
62.9
57.1
10.4
59.9
69.6
64.6
coutions
5.0
55.7
20.8
55.8
13.6
57.8
10.8
68.4
7.5
67.0
11.6
60.9
vanilla
5.0
55.1
22.8
54.3
13.7
54.3
11.8
64.6
7.8
54.2
12.2
56.5
confident
4,9
53.8
23.0
56.7
14.2
53.3
12.4
66.1
10.9
49.9
13.1
56.4
very confident
4.6
52.3
24.6
56.6
14.5
55.6
12.3
66.6
11.5
50.1
56.2
SteerConf
64.7
10.0
70.7
4.3
74.5
81.2
Table 10: Single steering prompt performance on LLaMA3 with CoT. Lower ECE and higher AUROC better.
Method
GSM8K
Law
DateUnd
StrategyQA
Ethics
Avg
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
ECE
AUROC
StecrConf (no, verys) =)
3.0
76.8
14.7
64.4
11.2
69.4
7.5
71.6
8.0
72.8
8.9
71.0
SteerConf (no_ mild)
8.4
78.8
6.8
62.8
10.4
67.4
7.2
73.7
16.3
77.7
9.8
72.1
SteerCoul = 5)
6.1
81.2
3.3
64.7
10.0
70.7
74.5
13.6
81.2
7.5
74.5
Table 11: Comparison of steering levels on LLaMA3 (CoT): 5 vs. & 3. Lower ECE and higher AUROC are better.
GSMSK
DateUnd
ObjCnt
Strategy
Sport
Law
Ethics
Avg
GPT3.5
78.3
71.5
58.1
68.4
63.3
46.7
67.7
64.9
SSteerConf
72.1
$6.9
65.0
47.7
61.4
Acef
+SteerConf (Majority)
78.5
77.2
63.8
68.3
64.2
49.8
66.0
66.8
LLaMA3
94.9
88.3
91.3
79.6
79.5
64.9
83.0
83.1
*SteerConf
94.6
87.0
91.3
77.4
78.4
65.3
82.0
82.3
+SleerConf (Majority)
95.9
88.6
94.1
78.7
81.1
65.5
81.0
83.6
GPT3.5
20.3
30.8
41.8
26.0
20.5
44.3
24.8
29.8
+StcerConf
5.4
6.7
19.7
14.5
10.8
16.0
15.1
12.6
BCE]
SSieerConf (Majority)
9.4
9.0
13.5
14.5
15.6
14.8
13.8
12.9
LLaMA3
5.0
13.7
8.7
11.8
22.8
11.1
+SteerConf
6.1
10.0
2.7
4.3
11.0
3.3
13.6
7.3
+SteerConf (Majority)
9.9
2.4
13.7
3.3
13.2
7.8
GPT3.5
56.2
49.8
50.1
56.4
62.7
53.0
65.2
56.2
SteerCoonf
84.9
76.0
82,5
67.5
66.4
61.5
86.7
75.1
CSterCoonf (Majority)
84.2
72.9
78.1
64.0
60.5
72.1
AUROCT
LLaMA3
$5.1
54.3
50.0
64.6
74.1
$4.3
$4.2
58.1
*SteerConf
74.5
77.0
SteerConf (Majority)
75.1
67.3
82.4
72.9
75.4
64.2
82.8
74.3
GFT3.5
26.5
28.0
42.0
36.8
46.9
55.5
45.7
40.2
CSteeConnf
72.3
49.7
74.7
48.9
$2.2
60.9
62.0
*SteerConf (Majority)
65.0
42.5
63.6
42.2
45.8
58.2
60.3
53.9
PR-N
LLaMA3
13.4
8.7
29.7
38.3
38.2
23.8
*SteerConf
41.3
24.0
49.0
43.2
47.5
50.6
51.7
43.9
SieerCoonf (Majority)
21.8
19.1
33.8
39,9
37.0
48.3
58.8
37.0
GPT3.5
80.5
71.5
$8.2
71.5
70.2
48.4
75.8
68.0
+SteerConf
90.8
88.4
83.2
77.7
75.0
$7.3
91.8
80.6
*SteerConf (Majority)
93.0
89.3
83.4
78.0
76.9
58.
90.3
81.3
PR-PT
LLaMA3
95.4
89.3
91.3
85.2
89.2
66.5
83.0
85.7
+StecrConf
97.8
92.6
98.1
90.0
92.8
73.6
93.1
91.1
+SteerConf (Majority)
97.8
92.8
98.1
92.7
73.5
93.2
91.2
Table 12: Performance comparison across datasets for SteerConf variants with different answer selection methods. Results include GPT-3.5 and LLaMA3, both under CoT settings.
27


Reviews:
[{"review_id": "qNyWHQ2Tpx", "reviewer": "Reviewer_ZMK7", "summary": "This paper proposes a new approach to obtain verbalized confidence scores that are better calibrated. Specifically, the approach prompts the model through different prompts and obtains a list of paired answers and confidence scores. Then the authors propose a way to aggregate these pairs to obtain a final confidence score. This approach demonstrate better calibration performance than the vanilla verbalized confidence scores and several other baselines.", "questions": "NA", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "#### Strengths\n\n1. This paper studies an important problem to calibrate LLMs’ confidence scores, which could be helpful to control hallucination and risks.  \n2. The proposed approach is training-free and can be easily adopted.\n\n\n#### Weaknesses\n\n1. This paper proposes a new way to aggregate confidence and answer pairs, and it should compare with stronger baselines such as Avg-Conf and Pair-Rank in [1] – which is necessary also because the evaluation datasets of the two works are similar.   \n2. The proposed approach has several components, and I think it should be made clear the effectiveness of each component. For example, (1) how necessary is it to use carefully designed steering prompts? Would just semantically similar prompts suffice? Or maybe we can just use one prompt but sample multiple different responses? (2) Controlling the prompts fixed, how effective of different aggregation approaches?", "was_revised": true, "final_justification": "This paper is limited at novelty in general -- methodologically it is like a new way of aggregating different verbose confidence scores. Several similar works exist previously, and the originality is not significant enough. The original submission is not at good quality I would say, as in my review, it lacks some baselines and proper ablations for the design choices, thus I gave an initial rating of 2. \n\nThe authors added additional baselines and ablation results in the rebuttal which addressed my concerns on the experiments, but my concern on the originality of the paper persists. I think this paper is very borderline and I don't have clear preference, I raise my score to 3 because the initial version was not good, but the AC or the authors can interpret my score as 3.5.", "rebuttal": "# Response to reviewer **ZMK7**\nWe have addressed all your comments below with experiments. Thank you for the constructive comments.\n \n**W1: compare with baselines such as Avg-Conf and Pair-Rank**\n\n**Response:** \n1. As noted in Line 267, the \"Top-K\" baseline already incorporates the Pair-Rank aggregation, which is specifically tailored for the Top-K approach.\n2. For Avg-Conf, we report results using the LLaMA3-70b-4bit backbone with CoT in the table below. The results show that our proposed method outperforms all baselines, including Self-Random and Misleading variants with Consistency, Pair-Rank, and Avg-Conf aggregation. For instance, SteerConf achieves ECE of 7.5, notably better than Self-Random with Avg-Conf that is 14.3.\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Vanilla | 5.0 | 55.1 | 22.8 | 54.3 | 13.7 | 54.3 | 11.8 | 64.6 | 7.8 | 54.2 | 12.2 | 56.5 |\n| Misleading+Consistency | 4.4 | 83.8 | 18.9 | 59.7 | 18.4 | 67.8 | 11.7 | 65.3 | 14.0 | 75.0 | 13.5 | 70.3 |\n| Self-Random+Consistency | 2.2 | 79.3 | 27.1 | 64.6 | 7.2 | 66.8 | 17.3 | 60.1 | 15.9 | 55.3 | 13.9 | 65.2 |\n| Misleading+Avg-Conf | 3.3 | 83.5 | 18.8 | 62.4 | 15.6 | 70.4 | 11.1 | 67.8 | 14.9 | 75.2 | 12.8 | 71.9 |\n| Self-Random+Avg-Conf | 2.3 | 79.3 | 27.1 | 64.4 | 8.8 | 68.3 | 17.2 | 59.9 | 16.3 | 55.0 | 14.3 | 65.4 |\n| Top-K+Pair-Rank | 10.2 | 61.1 | 23.0 | 49.7 | 27.5 | 52.2 | 21.1 | 55.0 | 11.1 | 52.4 | 18.6 | 54.1 |\n| Top-K+Avg-Conf | 56.4 | 60.6 | 15.1 | 55.8 | 40.7 | 55.9 | 36.4 | 52.0 | 5.2 | 67.5 | 30.7 | 58.4 | \n| SteerConf | 6.1 | 81.2 | 3.3| 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |\n\n**W2: make clear the effectiveness of each component. (1)  steering prompts (2) different aggregation approaches**\n\n**Response:** \nWe perform an ablation study to assess the impact of steering prompting and aggregation components, using LLaMA3-70b-4bit with CoT as the backbone. For (1), removing steering prompting yields \"SteerConf w/o Steering Prompting\". For (2), we replace our proposed aggregation with Consistency and Avg-Conf, denoted as \"SteerConf+Consistency\" and \"SteerConf+Avg-Conf\". Ablating both components results in the Self-Random approach with Consistency or Avg-Conf aggregation.  \nThe results are reported in the table below, which demonstrates that each component contributes to improved performance, supporting the effectiveness of our method’s design.\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| SteerConf w/o Steering Prompting | 2.3 | 80.0 | 13.2 | 66.7 | 10.0 | 70.2 | 6.1 | 72.5 | 10.2 | 59.0 | 8.4 | 69.7 |\n|Self-Random+Consistency | 2.2 | 79.3 | 27.1 | 64.6 | 7.2 | 66.8 | 17.3 | 60.1 | 15.9 | 55.3 | 13.9 | 65.2 |\n| SteerConf+Consistency | 2.4 | 71.7 | 25.9 | 63.8 | 7.5 | 68.2 | 17.2 | 61.4 | 14.3 | 68.5 | 13.5 | 66.7 | \n| Self-Random+Avg-Conf | 2.3 | 79.3 | 27.1 | 64.4 | 8.8 | 68.3 | 17.2 | 59.9 | 16.3 | 55.0 | 14.3 | 65.4 |\n| SteerConf+Avg-Conf | 2.8 | 71.3 | 25.8 | 63.3 | 8.6 | 67.1 | 17.4 | 61.3 | 16.7 | 73.5 | 14.3 | 67.3 |\n| SteerConf | 6.1 | 81.2 | 3.3 | 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |"}, {"review_id": "Lyjanlbf4H", "reviewer": "Reviewer_1saN", "summary": "This paper introduces SteerConf, a novel black-box framework for improving the confidence calibration of large language models via semantic steering. Instead of relying on internal logits or fine-tuning, SteerConf applies a set of carefully crafted prompts with varying confidence attitudes to elicit multiple predictions and verbalised confidence scores. The method computes a final calibrated confidence by aggregating the average confidence with both answer and confidence consistency metrics. Extensive experiments on seven benchmarks using GPT-3.5, GPT-4, and LLaMA3 demonstrate that SteerConf consistently improves Expected Calibration Error (ECE) and failure prediction metrics compared to vanilla verbalised confidence and other existing baselines.", "questions": "1.\tHave the authors conducted any ablation studies to assess the individual impact of each steering prompt?\n2.\tHow does the framework perform when applied to tasks with longer reasoning chains or in the CoT setting? Is there any further analysis in such cases?\n3.\tHow does SteerConf perform when the prompts intentionally guide the model to express overly high or low confidence? Is the method robust to such suggestive or misleading prompt cues?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.\tThe framework works in a black-box setting without requiring internal access or model fine-tuning.\n2.\tThe method is conceptually simple and easy to apply to any LLM with API access.\nWeaknesses：\n1.\tAlthough the proposed confidence consistency metric captures numerical stability across prompts, it does not consider potential semantic differences in the generated answers. In tasks involving more open or ambiguous questions, different prompts may lead to answers with similar confidence scores but different meanings. This aspect is not sufficiently discussed in the paper.\n2.\tThe method requires multiple LLM calls per input but does not analyse whether all prompts are necessary. Without understanding each prompt’s contribution, the method may be less efficient than necessary.", "was_revised": true, "final_justification": "The authors carefully responded to my previous questions. Based on the explanation and more experimental results, I have decided to raise my score.", "rebuttal": "# Response to reviewer 1saN\nWe thank the reviewer for the insightful comments and we have addressed them as follows.\n\n**W1: semantic differences in the generated answers, more open or ambiguous questions**\n\n**Response:** \nWe clarify that, following prior work, our current tasks focus on numerical answers (GSM8K) or answers with predefined options (Law), where ambiguity is minimal and answer consistency is both sufficient and effective. We agree that addressing semantic differences in generated answers is important for more open-ended tasks, and will include this discussion as future work in the revised version.\n\n**W2: Each prompt’s contribution**  \n**Q1:  individual impact of each steering prompt**\n\n**Response:** \nAs suggested, to assess the contribution of each steering prompt, we conduct an ablation study by removing each prompt individually using LLaMA3-70b-4bit with CoT as the backbone. For example, \"SteerConf w/o very_confident\" excludes the very_confident prompt. The results are shown in the table below. Notably, the cautious prompts (cautious, very_cautious) primarily improve AUROC, while the confident prompts (confident, very_confident) mainly enhance ECE.\n\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| SteerConf w/o very_confident | 7.2 | 80.9 | 5.1 | 64.3 | 11.1 | 70.9 | 5.8 | 74.3 | 17.9 | 80.4 | 9.4 | 74.1 |\n| SteerConf w/o confident | 7.4 | 80.8 | 5.3 | 64.6 | 11.1 | 69.8 | 5.9 | 74.3 | 16.0 | 81.4 | 9.1 | 74.2 |\n| SteerConf w/o vanilla | 6.8 | 79.3 | 4.4 | 64.2 | 11.4 | 68.5 | 5.8 | 74.4 | 12.9 | 78.7 | 8.3 | 73.0 |\n| SteerConf w/o cautious | 6.9 | 80.7 | 4.7 | 63.8 | 9.3 | 68.3 | 5.3 | 74.2 | 12.9 | 77.2 | 7.8 | 72.8 |\n| SteerConf w/o very_cautious | 3.1 | 78.0 | 13.3 | 65.7 | 11.3 | 69.9 | 7.6 | 72.4 | 6.3 | 78.0 | 8.3 | 72.8 |\n| SteerConf | 6.1 | 81.2 | 3.3 | 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |\n\n**Q2: Perform with CoT setting**\n\n**Response:** \nWe highlight that Table 2 in our paper (Page 7, above line 260) presents results for the CoT setting across different model backbones. CoT prompting consistently enhances the performance of all LLMs. Importantly, SteerConf continues to outperform the vanilla verbalized confidence approach, confirming the effectiveness of our method regardless of CoT usage.\n\n**Q3: How does SteerConf perform when the prompts intentionally guide the model to express overly high or low confidence?**\n\n**Response:** \nThe design principle of our method SteerConf is precisely to intentionally **steer the confidence** output of LLMs. SteerConf effectively handles cases when prompts intentionally guide the model to express overly high or low confidence. In fact, this intentional steering mechanism is the key design of our SteerConf (Steering Confidence) method as stated in Sections 1 and 3, where \"steering\" refers to: generating confidence scores in a specified direction (e.g., conservative or optimistic) by employing prompts with varying degrees of caution or confidence. Specifically, we apply steering prompts like \"You should adopt a very cautious approach and assign low confidence to most of your answers\" to guide the LLM to express deliberately calibrated confidence levels. The steered confidence scores and answers are then aggregated to obtain more reliable confidence estimates. \nWe will emphasize this point in the revised version."}, {"review_id": "TRW0hguXHd", "reviewer": "Reviewer_NUu4", "summary": "This paper introduces a method to enable large-language models to output a calibrated confidence score alongside answers to questions. The method implemented builds upon previous work in this area, where answer consistency has been used as a measure of confidence, however here the authors also introduce confidence consistency, based on the consistency of confidence scores, when models are steered towards different degrees of confidence. This provides 2 signals for confidence that together in concert may provide a more reliable measure of confidence. The authors demonstrate that the confidence scores output using their approach are better calibrated than other approaches. Additionally, by using their score to select answers, the authors demonstrate an improvement in performance. This work has important implications for high stakes domains, where confidence may be an important component of decision-making.", "questions": "1. Did the authors experiment with a greater/different number of steering levels in the prompting?\n2. Did the authors consider weighting the confidence and answer consistency terms in their confidence measure to observe how this changed the results?", "limitations": "This method only works in the simple closed-form question/answering setting. The authors could be more clear about this limitation, pointing out that this method would not work (or would require adaptation) for cases where a language-model is outputting a free-form longer response to a question.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n* The ability to generate reliable confidence scores with black-box language-models is an important area of research and the authors make a notable contribution to this field.\n* The confidence score is well designed with a clear rationale and appears to offer an improvement compared to other scores used in past research.\n* The score is relatively well validated, comparing their approach to several competing approaches, and demonstrating an improvement across multiple dimensions.\n\n## Weaknesses\n### Major\n* The authors discuss the improvements in confidence calibration offered by their method however they only consider one metric for calibration (expected calibration error). It might be more informative to also use calibration curves to better understand the nature of the improvement in calibration. See [1] for a description of different types of calibration. I think figure 4 is less relevant for the main results and could be moved to the appendix to make space for more interrogation of calibration here.\n* It’s not clear that these results would hold across many model-types. The results only compare 3 models, and 2 of these are both from OpenAI, so are likely trained in a similar manner. It would be useful to know whether this approach also works for smaller models, like Llama-8b models or other model types.\n* Steerconf is only compared to the vanilla confidence level, however Figure 1 clearly shows that a cautious prompt gives better results. It would be good to compare this approach to more cautious prompts, as a more difficult baseline, to demonstrate that steerconf does offer an improvement to just using more cautious prompts.\n### Minor\n* In the introduction (line 66), the authors state “the very cautious prompt improves calibration, achieving a 29% increase in Area Under the Receiver Operating Curve” however AUROC does not measure calibration, so please amend this to be clear.\n* The Related Work section needs to mention some of the work that informs the baselines they later use as part of their validation (such as [37])\n* The introduction to the overall method (lines 138-147) could be rephrased to better justify the rationale to the approach. Lines 217-225 very convincingly and clearly explained why the measure was defined as it was and could be moved to the introduction to better introduce the approach.\n\n[1] Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol. 2016 Jun;74:167-76. doi: 10.1016/j.jclinepi.2015.12.005. Epub 2016 Jan 6. PMID: 26772608.", "was_revised": true, "final_justification": "I have updated my score to reflect the author's response to the weaknesses highlighted in my original review.\n\nOverall, I think this paper introduces a novel method for improving confidence scores in language-models which is an important issue deserving attention. The paper is not without its limitations, though the authors address these in the paper and in their response. In the future I hope to see more research build upon the findings presented in this paper to further advance the field of confidence elicitation in language-models.", "rebuttal": "# Response to reviewer **NUu4**\nWe thank the reviewer for the insightful comments. We have addressed all comments with experiments and clarifications.\n\n**W1: calibration curves**\n\n**Response:** \nWe will add the calibration curves and move Figure 4 to the appendix as suggested by the reviewer. As external URLs to diagrams are not permitted in the rebuttal, these updates will be made in the revision.\n\n**W2: smaller and different model**\n\n**Response:** \nAs suggested, we conduct experiments using the small model Qwen3-1.7b under the CoT setting, as shown in the table below. Due to its relatively weaker instruction following ability, frequent collapsed verbalized confidence elicitation renders results on some datasets (Law and Ethics) unusable; thus, we report results only on datasets with reliable responses. The results demonstrate that our proposed method consistently outperforms the vanilla verbalized confidence approach under the CoT setting, even with a small model backbone.\n\n|Method|GSM8K ECE|GSM8K AUROC|Sport ECE| Sport AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|ObjCnt ECE|ObjCnt AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Qwen3-1.7b Vanilla | 25.5 | 54.7 | 37.2 | 56.0 | 45.5 | 62.4 | 37.2 | 55.5 | 32.4 | 65.3 | 35.5 | 58.8 |\n| Qwen3-1.7b + SteerConf | 8.3 | 80.4 | 40.1 | 49.8 | 46.5 | 62.3 | 39.2 | 51.1 | 22.0 | 74.5 | 31.2 | 63.6 |\n\n\n**W3: compare this approach to more cautious prompts**\n\n**Response:** \nAs suggested, we report results using only the very_cautious, cautious, confident, and very_confident prompts in the LLaMA3-70b-4bit with CoT setting, respectively. SteerConf consistently achieves the best overall performance in terms of average ECE and AUROC, though a single prompt could outperform on specific metric. We will include this comparison in the revised version as recommended by the reviewer.\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| vanilla | 5.0 | 55.1 | 22.8 | 54.3 | 13.7 | 54.3 | 11.8 | 64.6 | 7.8 | 54.2 | 12.2 | 56.5 |\n| very_cautious | 9.9 | 62.9 | 8.7 | 57.1 | 10.4 | 59.9 | 11.9 | 69.6 | 19.4 | 73.3 | 12.1 | 64.6 |\n| cautious | 5.0 | 55.7 | 20.8 | 55.8 | 13.6 | 57.8 | 10.8 | 68.4 | 7.5 | 67.0 | 11.6 | 60.9 |\n| confident | 4.9 | 53.8 | 23.0 | 56.7 | 14.2 | 55.3 | 12.4 | 66.1 | 10.9 | 49.9 | 13.1 | 56.4 |\n| very_confident | 4.6 | 52.3 | 24.6 | 56.6 | 14.5 | 55.6 | 12.3 | 66.6 | 11.5 | 50.1 | 13.5 | 56.2 |\n| SteerConf | 6.1 | 81.2 | 3.3 | 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |\n\n\n\n**Minor W4 & Minor W6:**\n\n**Response:** \nAs suggested, we will rephrase the relevant sentence to improve presentation.\n\n**Minor W5: discuss a related work**\n\n**Response:** \nAs suggested, we will discuss the suggested related work. \n \n**Q1: experiment with a greater/different number of steering levels**\n\n**Response:** \nAs suggested, we vary the number of steering levels from $5$ to $3$ to assess their impact, using LLaMA3-70b-4bit with CoT. In the table, \"SteerConf-no_verys\" omits the very_cautious and very_confident prompts, while \"SteerConf-no_mild\" omits the cautious and confident prompts. The results show that $l=5$ yields better performance than $l=3$. In practice, $l=5$ offers a good balance, as increasing the number of levels incurs additional query cost. We will include this discussion in the revised version.\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| SteerConf-no_verys | 3.0 | 76.8 | 14.7 | 64.4 | 11.2 | 69.4 | 7.5 | 71.6 | 8.0 | 72.8 | 8.9 | 71.0 |\n| SteerConf-no_mild | 8.4 | 78.8 | 6.8 | 62.8 | 10.4 | 67.4 | 7.2 | 73.7 | 16.3 | 77.7 | 9.8 | 72.1 |\n| SteerConf | 6.1 | 81.2 | 3.3 | 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |\n\n\n\n**Q2: weighting the confidence and answer consistency terms**\n\n**Response:** \nWe clarify that our approach *multiplies* the confidence and answer consistency terms, $c(x)=\\mu_c\\cdot \\kappa_{ans}\\cdot \\kappa_{conf}$ as shown in Equation (4) below line 225. This multiplicative design avoids the need to manually balance the two terms, and leverages the transferability advantage of multiplication. Introducing a weight parameter would not directly assign a specific weight to either term."}, {"review_id": "p8d3bpZMvD", "reviewer": "Reviewer_BqFQ", "summary": "This paper proposes SteerConf, a framework to improve confidence calibration in LLMs by steering their confidence scores via prompts. It introduces three components: (1) a steering prompt strategy; (2) a steered confidence consistency measure; and (3) a steered confidence calibration method. Without additional training or fine-tuning, SteerConf reduces calibration errors and improves failure detection across 7 benchmarks.", "questions": "1. How much impact does the setting of $l$ in the steering level have on the results, or as least how much $l$ is needed to achieve good results?\n2. What does the argument in Line 186-187 mean, by saying \"howerver overlooks the consistency of the verbalized confidences returned by LLMs, which may contain rich information of model's interior certainty\"?\n3. When using different prompts to guide the LLM's confidence elicitation, have the authors considered the influence of LLM's instruction following ability on these results?\n4. The tested LLMs in this paper are all with large-scale parameters. What will the performance of LLMs with relatively less parameters (like 7B, 32B, etc.) be like? (As it is time-consuming to conduct experiments during the rebuttal, the authors can first discuss the relevant results.)", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n1. The idea of steering LLM confidence through carefully designed prompts is simple yet underexplored. And the SteerCof method does not require training or fine-tuning, making it applicable to black-box commercial LLMs.\n2. This paper presents thorough experiments and evaluations across 7 diverse benchmarks (including commonsense, math, ethics, etc.) with multiple LLMs (GPT-3.5, GPT-4, LLaMA 3), demonstrating broad application of the framework.\n\n**Weaknesses**\n\n1. The proposed method requires multiple LLM calls per query, which is costly and makes it hard to extend to complex multi-turn reasoning tasks.\n2. The steering prompts are manually designed. Small changes in these prompts might affect the final performance, but no robustness analysis is provided.\n3. The answer selection process in Sec 3.3 is not intuitive. As claimed before that confidence scores elicited from LLMs are often overestimated, matching calibrated confidence $c(x)$ with steered confidence scores to select the final answer seems not quite reasonable.", "was_revised": true, "final_justification": "The authors' rebuttal has addressed most of my previous concerns. However, as the authors stated that \"our primary focus is on widely-used LLMs with reasonably good capabilities for black-box confidence calibration (e.g., GPT-3.5, LLaMA3, GPT-4)\", it implies that the SteerConf is a method relying on the strong reasoning capability of large language models, which limits it application to relatively small models (like 7B, 14B, etc.). \n\nOverall, according to the novelty of the paper, the experimental results, and the authors' rebuttal, I think it is a boardline paper. So I rate it 4.", "rebuttal": "# Response to reviewer **BqFQ**\nWe thank the reviewer for the insightful comments. We have addressed them with clarifications and experiments.\n\n**W1: multiple LLM calls per query; complex multi-turn reasoning tasks.**\n\n**Response:** \nWe clarify that multiple query-based calibration is a common practice in the literature [31,37]. In our setting, since we don't have access to the internal states of LLMs, additional cost with multiple calls represents a necessary and reasonable trade-off to alleviate this information gap. \nFor complex multi-turn reasoning tasks, our multi-query methodology can be viewed as a form of test-time scaling: investing more computational resources during inference to obtain better calibration and consequently more reliable reasoning traces.\n\n**W2: robustness of prompts**\n\n**Response:** \nWe provide experiments to demonstrate the robustness of our method to prompt variations. We modified the expression of steering prompts to be more concise while preserving the core steering intent. For example, we simplified the \"very_cautious\" prompt as follows:\n\n*Original(SteerConf)*: Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) You are making important decisions, thus you should avoid giving a wrong answer with high confidence. (3) You should be very cautious, and tend to give low confidence on almost all of the answers.\n\n*Modified(SteerConf_brief)*: Read the question, provide your answer and your confidence in this answer. Note: (1) The confidence indicates how likely you think your answer will be true. (2) Be very cautious, tend to give very low confidence on every answer. \n\nWe compare the modified and original versions of SteerConf with backbone LLM GPT-3.5 without CoT setting in the table below. Notably, SteerConf_brief achieves even lower ECE than SteerConf, and both outperform GPT-3.5-vanilla. These results demonstrate the robustness of our proposed method to prompt variations.\n\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| GPT-3.5-vanilla | 62.6 | 55.8 | 43.2 | 51.7 | 60.2 | 56.6 | 29.6 | 53.3 | 26.0 | 54.8 | 44.3 | 54.4 |\n| SteerConf_brief | 9.6 | 82.4 | 9.3 | 61.3 | 12.1 | 55.0 | 21.0 | 58.4 | 14.8 | 68.1 | 13.3 | 65.0 |\n| SteerConf | 22.8 | 82.9 | 24.0 | 60.6 | 33.0 | 60.5 | 14.9 | 58.6 | 10.8 | 71.0 | 21.1 | 66.7 |\n\n\n**W3: The answer selection process in Sec 3.3 is not intuitive**\n\n**Response:** \nOur answer selection mechanism is motivated by the principle that answers with higher calibrated confidence should be selected when the model is more certain, and vice versa. Specifically, we compare the *relative rank* of confidence scores among candidate answers, as formalized in equation (6). Experimental results in Table 5 demonstrate that this selection strategy improves calibration. We will clarify this point in the revised version.\n\n**Q1: setting of the steering level**\n\n**Response:** \nTo investigate the impact of steering levels $l$, we vary $l$ from $5$ to $3$ using LLaMA3-70b-4bit with CoT, and report results in the table below. \"SteerConf-no_verys\" excludes very_cautious and very_confident prompts, while \"SteerConf-no_mild\" excludes cautious and confident prompts. The results show that $l=5$ yields better performance than $l=3$. Increasing $l$ may further improve results but incurs higher query cost and requires designing more fine-grained prompts, which is non-trivial. In practice, we find $l=5$ offers a good balance. We will add this discussion in the revised version.\n\n|Method|GSM8K ECE|GSM8K AUROC|Law ECE|Law AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|Ethics ECE|Ethics AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| SteerConf-no_verys | 3.0 | 76.8 | 14.7 | 64.4 | 11.2 | 69.4 | 7.5 | 71.6 | 8.0 | 72.8 | 8.9 | 71.0 |\n| SteerConf-no_mild | 8.4 | 78.8 | 6.8 | 62.8 | 10.4 | 67.4 | 7.2 | 73.7 | 16.3 | 77.7 | 9.8 | 72.1 |\n| SteerConf | 6.1 | 81.2 | 3.3 | 64.7 | 10.0 | 70.7 | 4.3 | 74.5 | 13.6 | 81.2 | **7.5** | **74.5** |\n\n\n\n**Q2: Clarify the argument in Line 186-187**\n\n**Response:** \nWe clarify that existing study [37] focuses on answer consistency, not confidence score consistency. We argue that a model's internal certainty is reflected in both answer and confidence consistency. Therefore, our method is designed to consider both aspects. Empirical results in the experiments section validate this approach.\n\n**Q3:  influence of LLM's instruction following ability**\n\n**Response:** \nPrior verbalized confidence works [31,37] did not address the impact of LLMs' instruction following ability. In our setting, the vanilla verbalized confidence method [31,37] already poses a challenge: if an LLM (e.g., small LLaMA-2 models) has poor instruction following ability, it may fail to produce answer-confidence pairs in the required format. Once an LLM can reliably perform verbalized confidence tasks, its instruction following ability is sufficient for our steering confidence instructions. Figure 4 demonstrates that our steering prompts are successfully followed by current LLM backbones. We will clarify this point in the revised version.\n\n\n**Q4: smaller models**\n\n**Response:** \nAs suggested, we conduct experiments using the small model Qwen3-1.7b under the CoT setting, as shown in the table below. Due to the model's relatively weaker instruction following ability, frequent collapsed verbalized confidence elicitation renders results on some datasets (Law and Ethics) unusable; thus, we report results only on datasets with reliable responses. The results demonstrate that our proposed method consistently outperforms the vanilla verbalized confidence approach under the CoT setting, even with a small model backbone.\n\n|Method|GSM8K ECE|GSM8K AUROC|Sport ECE|Sport AUROC|DateUnd ECE|DateUnd AUROC|StrategyQA ECE|StrategyQA AUROC|ObjCnt ECE|ObjCnt AUROC|Average ECE|Average AUROC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| Qwen3-1.7b Vanilla | 25.5 | 54.7 | 37.2 | 56.0 | 45.5 | 62.4 | 37.2 | 55.5 | 32.4 | 65.3 | 35.5 | 58.8 |\n| Qwen3-1.7b + SteerConf | 8.3 | 80.4 | 40.1 | 49.8 | 46.5 | 62.3 | 39.2 | 51.1 | 22.0 | 74.5 | 31.2 | 63.6 |"}]

Author rebuttal:

Meta-review: This paper introduces SteerConf, a novel, training-free framework for improving the confidence calibration of black-box Large Language Models. The core idea is to use a set of carefully crafted prompts to steer the model into generating answers with varying levels of verbalized confidence (e.g., cautious, confident). The final calibrated confidence is then derived by aggregating these outputs, considering both the consistency of the answers and the consistency of the confidence scores themselves. The authors demonstrate through extensive experiments on seven diverse benchmarks that SteerConf significantly outperforms existing methods in calibration and failure detection. The primary strengths, as highlighted by the reviewers, are that the paper addresses an important and underexplored problem (ZMK7, NUu4, BqFQ), the proposed method is simple, practical, and broadly applicable as it is training-free and works in a black-box setting (1saN, ZMK7), and the claims are supported by consistent and significant empirical improvements (1saN, ZMK7, NUu4, BqFQ). The main weaknesses raised were concerns about the method's novelty, with one reviewer finding it an incremental aggregation technique (ZMK7), its reliance on the instruction-following capabilities of large models which may limit its effectiveness on smaller models (BqFQ), and the increased computational cost due to multiple queries per input (BqFQ). The decision to accept is based on the paper's clear and practical contribution, validated by a strong set of experiments. The authors' rebuttal was exemplary, providing substantial new experiments that addressed nearly all reviewer concerns, leading three of the four reviewers to either confirm a high score or raise their initial score.
Decision: Accept (poster)