---
title: Verbalized Confidence Scores in LLMs
url: https://www.emergentmind.com/topics/verbalized-confidence-scores
type: topic
---

# Verbalized Confidence Scores in LLMs

Verbalized confidence scores are scalar or categorical expressions of uncertainty produced in natural language alongside a model’s predictions, typically to communicate the system’s own estimate of probability of correctness. In large language models (LLMs) and related neural architectures, these scores replace or supplement internal "white-box" uncertainty measures such as logits or entropy with interpretable, user-facing outputs such as "Confidence: 73%". Methods for eliciting, calibrating, and exploiting verbalized confidence have become central for reliable deployment in high-stakes applications, particularly as these scores serve as both an uncertainty quantification mechanism and a user interface element. Calibration, robustness, and grounding of confidence expression—especially under conditions of input noise, adversarial attacks, or strategic risk—are active areas of research.

## 1. Formulation and Generation of Verbalized Confidence

Verbalized confidence is most commonly implemented as a numeric probability (e.g., "I am 80% confident that...") or as a verbal descriptor mapped to a probability (e.g., "Highly likely" → 0.9) [2412.14737][2305.14975]. In contemporary LLMs and agentic pipelines, the standard pattern is to prompt the model to output its answer and, in a distinct field, its confidence. Examples of prompt protocols include:

- **Closed-form scalar**: "Confidence: [0-100]%"
- **Probability distribution**: "Probability that each choice is correct: {A: 0.6, B: 0.3, ...}"
- **Linguistic descriptions**: "How likely is it? Options: Almost certain, Highly likely, ..." (with mappings to [0,1])

Both open-weight and closed-weight models (e.g., Llama, GPT-4) support natural-language confidence generation via templated prompts [2412.14737][2409.09629][2510.23458].

In settings such as retrieval-augmented generation (RAG), verbalized confidence is defined as a model-predicted scalar $\hat c \in [0,1]$ jointly output with answer $\hat a$, i.e., $(\hat a, \hat c) = f_\theta(q, P)$, where $q$ is a query and $P$ contains evidence passages [2601.11004]. In multi-turn or agentic workflows, confidence is usually recorded after all reasoning and tool use—a single final number reflecting cumulative epistemic uncertainty [2510.23458].

For classification or dialogue tasks, verbalized confidence often appears as a sub-token or explicit number in the output JSON or text blob; post-parsing yields the usable score [2409.09629]. In all cases, the reported confidence is model-generated and not an internal probability unless specifically calibrated.

## 2. Calibration: Metrics, Losses, and Empirical Findings

Calibration asks that, over all instances where the model says "X% confident," empirical accuracy matches $X\%$ [2305.14975][2412.14737]. The main evaluation metrics are:

- **Expected Calibration Error (ECE):**
  $$\mathrm{ECE} = \sum_{m=1}^M \frac{|B_m|}{N} |\mathrm{acc}(B_m) - \mathrm{conf}(B_m)|$$
  where $B_m$ is a confidence bin, $\mathrm{acc}(B_m)$ the empirical accuracy, and $\mathrm{conf}(B_m)$ the mean model-reported confidence in $B_m$.

- **Brier Score:**
  $$\mathrm{BS} = \frac{1}{N}\sum_i (c_i - y_i)^2$$
  proper for binary and multiclass calibration [2508.18847][2510.23458][2410.06707].

- **Discrimination (AUROC):**
  The area under the ROC curve of confidence as a predictor of correctness.

In practice, vanilla prompting of LLMs yields significant overconfidence, especially on difficult tasks or under irrelevant/contradictory context [2601.11004][2412.14737]. For example, off-the-shelf LLMs show ECE values of $\sim0.41$ in noisy retrieval, with calibrated frameworks such as NAACL [2601.11004] reducing ECE by $8{-}11\%$ absolute. Standard prompt methods (e.g., "confidence: [0-100]%") are sensitive to wording, with more explicit instructions and few-shot examples yielding better calibration for large models (ECE $\sim0.07$ in best cases) [2412.14737].

Calibration can be improved by:

- Proper scoring rule-based fine-tuning (e.g., tokenized Brier loss in ConfTuner [2508.18847])
- Temperature scaling, typically applied post-hoc to verbalized distributions [2410.06707]
- Contrastive objectives enforcing answer-conditioning (ADVICE [2510.10913])
- Synthetic noise injection or distractor normalization [2601.11004][2509.25532]
- Reinforcement learning for long-form generative calibration (LoVeC [2505.23912])

Well-calibrated models facilitate selective abstention, cascaded fallback, and downstream uncertainty-aware decision making, although most state-of-the-art LLMs remain overconfident in absolute terms [2508.18847][2510.10913][2601.07767].

## 3. Methodologies for Enhancing Verbal Confidence

Recent research has developed a variety of fine-tuning and inference-time strategies to improve verbalized confidence calibration and utility.

- **Supervised Fine-Tuning with Proper Scoring Rules:** ConfTuner introduces a tokenized Brier loss, shown to be a proper scoring rule for discrete verbalized tokens, incentivizing truthful self-estimation [2508.18847].

- **Noise-Aware Calibration:** NAACL defines principled rules (conflict independence, noise invariance, parametric fallback) for how confidences should behave under contradictory, irrelevant, or absent retrievals; these rules guide supervised fine-tuning via Brier loss [2601.11004].

- **Distractor-Normalized Coherence (DINCO):** To counter suggestibility bias, DINCO normalizes confidence on the main claim by aggregated confidence over self-generated distractors, thus penalizing overconfidence in the absence of unique content [2509.25532].

- **Steered Prompting and Consistency Aggregation (SteerConf):** By systematic prompt steering (e.g., "be very cautious"/"be very confident") and aggregating confidence-consistency signals, models can mitigate both overconfidence and instability in calibration [2503.02863].

- **Answer-Dependency Enforcement:** ADVICE explicitly trains models to ground their confidence in their chosen answer, penalizing answer-independence through contrastive JSD and margin losses. This form of answer-groundedness significantly reduces overconfidence [2510.10913].

- **Long-Form Generation with RL:** LoVeC applies on-policy and off-policy RL algorithms (GRPO, DPO) to train LLMs to append confidence tags per sentence, with rewards based on alignment to factuality labels provided by oracle verifiers [2505.23912].

Design choices include format (scalar vs. categorical), prompt sequence (single- or multi-stage), use of chain-of-thought for deeper uncertainty exploration [2505.23845][2511.14275], and whether confidence spans only the final answer or intermediate steps [2409.09629][2506.03723].

## 4. Robustness, Trustworthiness, and Pitfalls

Verbalized confidence remains vulnerable to several sources of unreliability:

- **Suggestibility and Surface Mimicry:** Models may anchor confidence on prompt structure or input claims, not on genuine content evidence—a phenomenon exacerbated by overexposure to generic "confidence-cue" expressions during training (TracVC [2601.10645]). Larger models can overfit stylistic certainty rather than content-grounded uncertainty.

- **Adversarial Manipulation:** Targeted perturbations (token substitutions, character-level bugs, prompt triggers) can significantly alter reported confidence without meaningfully changing the question, undermining reliability. Existing defense mechanisms such as perplexity filters, guardrails, and paraphrasing are largely ineffective [2507.06489].

- **Calibration Saturation and Overconfidence:** Standard verbalized confidence methods often collapse to coarse, saturated predictions (e.g., 0.9 or 1.0), limiting utility as ranking signals or for thresholding [2509.25532]. Methods like DINCO and multi-option verbalized probability distributions [2511.14275] increase granularity and allow finer thresholding.

- **Disconnect from Strategic Decision-Making:** Even with well-calibrated verbalized uncertainty, LLMs may not leverage their own confidence to adjust policies under task-specific risk, such as abstaining when error costs rise—highlighted by large utility/regret gaps in risk-sensitive evaluation frameworks (RiskEval [2601.07767]).

Recommendations to address these pitfalls include adversarially-informed calibration objectives, content-grounded fine-tuning, hybrid elicitation combining multiple uncertainty signals, and explicit abstention or selective prediction heads [2507.06489][2601.10645][2601.07767].

## 5. Applications and Practical Considerations

Verbalized confidence is directly integrated into a spectrum of statistical, interactive, and multi-agent applications:

- **Information Retrieval and RAG Systems:** Enables fall-back to parametric knowledge under noisy retrieval (NAACL [2601.11004]); mitigates overconfidence driven by retrieval noise.
- **Dialogue State Tracking and Conversational Agents:** Used to flag uncertain slot-value assignments and trigger user confirmation or fallback [2409.09629].
- **Tool-Using and Agentic LLMs:** Facilitates dynamic compute allocation and low-overhead test-time scaling in multi-turn scenarios (BrowseConf [2510.23458]).
- **Cascade Architectures:** Powers automatic rejection routing, self-correction loops, or cost-sensitive fallback to stronger models, improving both performance and efficiency [2508.18847].
- **Long-Form Content Generation:** Sentence-level confidence tags support fine-grained hallucination detection and credibility assessment [2505.23912].
- **ASR Systems:** Word-level confidence from models such as Whisper enables robust downstream error filtering in speech-to-text [2502.13446].

Implementers should attend to prompt design (explicit numerical probability preferred over linguistic gradations), enforce consistent answer/confidence formats, and apply reliability metrics (ECE, Brier, AUROC) on deployment distributions. Fine-tuned calibration (e.g., via ConfTuner) offers significant gains for critical deployments at low resource cost [2508.18847].

## 6. Limitations, Open Problems, and Future Directions

Despite substantial progress, significant limitations and research directions remain:

- **Generalization to Open-Ended and Long-Form Tasks:** Most results are for closed-form or short-answer scenarios; transfer to creative or subjective settings is not well-understood [2412.14737][2505.23912].
- **Prompt Fragility and Format Adherence:** LLMs frequently produce extraneous text, incorrect formats, or coarsely discretized values; supervised fine-tuning or prompt anchoring may mitigate these effects [2410.06707].
- **Calibration of Abstention and Action Policies:** Current models’ confidence is not directly actionable for selective abstention under dynamic risk, necessitating joint training of uncertainty and policy heads [2601.07767].
- **Content Grounding:** Increased focus is needed on aligning confidence generation mechanisms with content evidence rather than superficial stylistics or training frequency artifacts [2601.10645].
- **Multi-Model Uncertainty and Agent Collaboration:** Integration of confidence signals in multi-agent or consensus settings is largely unexplored; combining verbalized signals with logit-based or entropy-based uncertainty remains an open research topic [2412.14737].
- **Adversarial Defenses and Monitoring:** No effective adversarial defense exists for pure verbalized confidence; robust UQ may require continuous monitoring and hybridization with internal or probe-based uncertainty estimates [2507.06489].

Future work is likely to focus on richer, compositional calibration objectives, dynamic prompt adaptation, multi-turn and multi-agent confidence aggregation, and alignment of confidence-driven policies with real-world risk expectations.

---

#### References

- [2305.14975]: Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback  
- [2412.14737]: On Verbalized Confidence Scores for LLMs  
- [2409.09629]: Confidence Estimation for LLM-Based Dialogue State Tracking  
- [2508.18847]: ConfTuner: Training Large Language Models to Express Their Confidence Verbally  
- [2503.02863]: SteerConf: Steering LLMs for Confidence Elicitation  
- [2510.10913]: ADVICE: Answer-Dependent Verbalized Confidence Estimation  
- [2601.11004]: NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems  
- [2505.23912]: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation  
- [2601.10645]: Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs  
- [2509.25532]: Calibrating Verbalized Confidence with Self-Generated Distractors  
- [2511.14275]: Don't Miss the Forest for the Trees: In-Depth Confidence Estimation for LLMs via Reasoning over the Answer Space  
- [2507.06489]: On the Robustness of Verbal Confidence of LLMs in Adversarial Attacks  
- [2502.13446]: Adopting Whisper for Confidence Estimation  
- [2510.23458]: BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents  
- [2505.23845]: Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMs  
- [2601.07767]: Are LLM Decisions Faithful to Verbal Confidence?  
- [2410.06707]: Calibrating Verbalized Probabilities for Large Language Models  
- [2506.03723]: Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision

Source: https://www.emergentmind.com/topics/verbalized-confidence-scores