Confidence-Aware Response Generation (CARG)

• Confidence-Aware Response Generation (CARG) is a control principle that integrates explicit uncertainty signals into language model outputs to guide interventions such as prompt conditioning and reranking.
• Methodologies in CARG vary from using log probabilities and semantic clustering to hidden state activations, each affecting generation and retrieval processes differently.
• CARG techniques improve metrics like NDCG and calibration while addressing issues of response consistency and overconfidence, but they also reveal challenges in reliably measuring uncertainty.

Searching arXiv for the cited CARG-related papers to ground the article with current references. Confidence-Aware Response Generation (CARG) denotes a family of inference-time methods that condition language-model behavior on explicit uncertainty or confidence estimates. Across recent work, the term has been used for multi-turn response stabilization, usefulness-oriented reranking in retrieval-augmented generation (RAG), confidence-weighted aggregation of sampled reasoning chains, decoupled verbalized-confidence estimation, confidence-triggered abstention, and pre-generation routing (Li et al., 28 Mar 2025, Song et al., 6 May 2026, Razghandi et al., 20 Feb 2025, Li et al., 12 May 2026, M, 23 Sep 2025). The common premise is that answer generation should be guided not only by the candidate content itself, but also by a model- or pipeline-level estimate of how strongly that content is supported under the current evidence condition.

1. Conceptual scope and major variants

The literature does not treat CARG as a single canonical algorithm. Instead, it presents a set of confidence-conditioned control schemes that differ in where confidence is measured and where it is applied. In multi-turn dialogue, confidence is embedded into the conversational state so that subsequent turns condition on prior question–response–confidence tuples (Li et al., 28 Mar 2025). In RAG, confidence can be used to decide whether retrieval should be triggered at all, which retrieved documents should be promoted or demoted, and which sub-claims in a generated answer should be retained (Song et al., 6 May 2026, Jin et al., 8 Sep 2025, Feng et al., 26 Jun 2025). In reasoning settings, confidence can weight intermediate reasoning chains rather than treating all sampled chains equally (Razghandi et al., 20 Feb 2025). When token logits are unavailable, verbalized confidence becomes the user-facing uncertainty channel, and answer generation can be decoupled from confidence generation to reduce interference with answer accuracy (Li et al., 12 May 2026).

Variant	Confidence signal	Control locus
Multi-turn CARG	Log probabilities on “The correct answer: X”	Next-turn prompt conditioning
CAR	Semantic consistency change under query-only vs query-document inputs	Promote/preserve/demote reranking
CER	Confidence of critical intermediate answers	Confidence-weighted answer aggregation
ORCE	Verbalized confidence conditioned on fixed question–answer pairs	Confidence calibration and abstention
CBDR / hidden-state CARG	Hidden-state confidence before first answer token	Dynamic retrieval gating
Conformal-RAG	Retrieval-grounded sub-claim relevance with conformal thresholds	Claim filtering with reliability guarantees

This diversity suggests that CARG is best understood as a control principle rather than a single method: a system exposes an uncertainty surrogate, feeds that surrogate into an intervention policy, and changes retrieval, ranking, generation, or response release accordingly.

2. Confidence signals and formal definitions

The earliest explicit CARG formulation in the cited set computes internal model confidence from the log probabilities of a fixed diagnostic span. Under the standardized response prefix “The correct answer: X”, sequence-level confidence is the geometric mean of the predicted token probabilities over that span:

$$\operatorname{Conf}\!\left(r_i^{(k,j)}\right) \;=\; \exp\!\left(\frac{1}{|S|}\sum_{w\in S}\log p\!\left(w \mid \mathbf{w}_{<t}\right)\right).$$

Confidence is then inserted into the multi-turn state

$$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$

and the next response is generated as

$$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$

so that the model conditions on both previous content and previous confidence (Li et al., 28 Mar 2025).

CAR replaces token-logprob confidence with a semantic-consistency signal derived from multiple sampled answers. For any input $x$, the generator is sampled $k$ times, answers are clustered by strict bidirectional entailment, and confidence is defined as the maximum semantic-cluster proportion:

$\operatorname{Conf}_\phi(x)=\max_j p_j.$

This yields query-only confidence $C(q)$, query–document confidence $C(q,d)$, and a document usefulness signal $\Delta C(d)=C(q,d)-C(q)$. CAR therefore defines usefulness not as relevance alone, but as the degree to which a document reduces the generator’s uncertainty for the specific query (Song et al., 6 May 2026).

CER adopts a different granularity. It computes word-level confidence on critical intermediate answers—numerical answers in mathematical reasoning and proper nouns in open-domain generation—using token probabilities. The primary word confidence is multiplicative probability over the tokens of the word, and path confidence is a weighted mean favoring later steps:

$$C_{\text{chain}}=\frac{\sum_{j=1}^{m} j \cdot c_j}{\sum_{j=1}^{m} j}.$$

Final answers are then selected by confidence-weighted voting across sampled reasoning chains rather than by majority vote (Razghandi et al., 20 Feb 2025).

Later work broadens the notion of confidence beyond token probabilities. ORCE treats confidence as verbalized text conditioned on a fixed question–answer pair, specifically to support black-box settings where token logits are unavailable (Li et al., 12 May 2026). Other CARG variants infer confidence from hidden states captured at a specific layer and time point before generation begins, or from autoregressive activation sequences over answer tokens, treating confidence estimation as a learned sequence-classification problem (Jin et al., 8 Sep 2025, Huang et al., 15 Oct 2025). A recurring implication across these formulations is that “confidence” is an operational surrogate, not a universally agreed quantity.

3. Intervention mechanisms

The intervention policy is the second defining component of CARG. In the original multi-turn framework, confidence does not directly modify logits, temperature, top-$$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$0, top-$$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$1, or beam search. Instead, it influences future behavior through prompt conditioning: prior turns are represented as tuples $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$2, and the model implicitly decides whether to reinforce a high-confidence stance or re-evaluate when confidence is low (Li et al., 28 Mar 2025). This design keeps the method inference-only and avoids retraining.

CAR makes the intervention explicit. It introduces a query threshold $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$3 and a confidence margin $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$4. If the query-only baseline confidence is already high, the baseline ranking is preserved. Otherwise, each candidate document is assigned to a promote, preserve, or demote bin according to whether $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$5 exceeds, approximately matches, or falls below $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$6 by the margin $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$7. The final ranking is the baseline order stably sorted as $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$8, preserving the original relative order inside each bin. The paper interprets this as a conservative, discretized Bayesian posterior update in which the baseline ranking supplies the prior and semantic-consistency confidence supplies usefulness evidence (Song et al., 6 May 2026).

CER intervenes at answer aggregation rather than retrieval or prompt state. It samples $$h_t \;=\; \big\{(q_1, r_1, c_1), \ldots, (q_{t-1}, r_{t-1}, c_{t-1}), q_t\big\},$$9 reasoning paths, extracts critical intermediate answers from each path, computes a path confidence, groups paths by exact final answer string, and chooses

$$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$0

This replaces self-consistency’s equal-weight majority vote with a weighted sum in which more reliable chains contribute more heavily (Razghandi et al., 20 Feb 2025).

ORCE formalizes a decoupled two-stage architecture. Stage 1 generates an answer $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$1 from an answer LLM given $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$2. Stage 2 generates confidence conditioned on the fixed pair $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$3 using a separate confidence model or prompt. Confidence training is then performed with order-aware objectives such as a Spearman-correlation reward and DPO, so that higher estimated correctness likelihood is assigned higher verbalized confidence while answer parameters remain frozen (Li et al., 12 May 2026). This separation is meant to prevent calibration-oriented updates from perturbing the answer distribution.

A broader pattern emerges across these systems: CARG methods either embed confidence into the same generative trajectory, as in multi-turn prompting, or use confidence as a routing variable external to the answer trajectory, as in reranking, weighted voting, abstention, or two-stage confidence generation.

4. Retrieval-augmented and retrieval-dependent CARG

Retrieval-centric CARG has developed along several distinct axes. CAR is the clearest reranking formulation. It is query-guided, training-free, and plug-and-play; it reranks the top-10 documents by measuring how much each candidate changes generator confidence relative to a query-only baseline. Experiments on four BEIR datasets—NQ, FEVER, SCIDOCS, and TREC-COVID—show that CAR consistently improves NDCG@5 across sparse and dense retrievers, LLM-based and supervised rerankers, and four LLM backbones. Under Contriever retrieval, the most notable gain is for the YesNo reranker, with average +25.4% relative improvement, and ranking gains correlate strongly with downstream generation quality at Spearman $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$4 on NQ with BM25 (Song et al., 6 May 2026).

A second line of work uses hidden-state confidence to decide whether retrieval should happen at all. In the post-retrieval confidence framework built around Confidence-Based Dynamic Retrieval (CBDR), confidence is the probability assigned by a classifier $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$5 to a pre-first-token Mid_Layer hidden state. If $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$6 exceeds a threshold $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$7, retrieval and reranking are skipped; otherwise, the system retrieves, reranks, and generates with external contexts. With the fine-tuned reranker and CBDR on NQ, $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$8 yields retrieval reduction of 83.30% and $$r_t \;=\; \arg\max_{r} \; P\!\left(r \,\middle|\, h_t, \theta, c_{t-1}\right),$$9 yields 92.90%, while maintaining competitive accuracy and even improving Top-3 accuracy in one setting (Jin et al., 8 Sep 2025).

Conformal-RAG applies CARG at sub-claim granularity. Generated answers are decomposed into refined sub-claims, each sub-claim receives a retrieval-grounded relevance score

$x$0

and conformal thresholds are calibrated so that the retained set satisfies marginal or group-conditional coverage guarantees. At the same factuality guarantee, Conformal-RAG retains up to 60% more high-quality sub-claims than Conformal-LLM; on FActScore at target 85% factuality, it removes 8.9% of sub-claims versus 86.8% for Conformal-LLM (Feng et al., 26 Jun 2025). This is a CARG variant in which confidence is attached to retained claims rather than to a single answer token or sequence.

Confident RAG uses multiple embedding models rather than a single retriever. It runs RAG once per embedding model, generates one answer per retrieved context, scores each candidate answer with a token-probability-based confidence metric such as Self-Certainty or Distributional Perplexity, and returns the answer with highest confidence. On GSM8K, Confident RAG improves by approximately 10% over vanilla LLMs and 5% over vanilla RAG on average, while gains saturate at about $x$1 embedding models (Chen et al., 23 Jul 2025). A plausible implication is that retrieval-stage CARG can operate either before retrieval, during reranking, during answer selection, or after generation at the level of refined claims.

5. Multi-turn consistency, adversarial pressure, and failure cases

The multi-turn formulation of CARG was introduced to address response vacillation under follow-up pressure. The MT-Consistency benchmark draws from MMLU, CommonsenseQA, and TruthfulQA, uses seven follow-up scenarios, and evaluates consistency under repetitive and diverse follow-ups with $x$2, $x$3, and $x$4. On this setup, CARG improves stability without sacrificing accuracy. In the diverse follow-up experiment, mean accuracy is 0.7482 with $x$5, compared with 0.7134 for GPT-default and 0.7068 for GPT-adversarial, and the gains are reported as statistically significant at $x$6 by paired $x$7-test (Li et al., 28 Mar 2025).

The same paper also introduces Position-Weighted Consistency,

$x$8

which gives earlier turns larger weight and discounts later recovery. That metric reflects the motivating intuition of CARG in sequential settings: early sustained correctness is more valuable than late correction after repeated sway (Li et al., 28 Mar 2025).

A major controversy arises in reasoning models. Under multi-turn attacks on nine frontier reasoning models, the CARG mechanism that had helped instruction-tuned LLMs no longer improved robustness. Confidence–correctness correlation becomes $x$9 with $k$0, ROC-AUC is 0.54, and the confidence distribution is highly compressed with mean 96.1%, standard deviation 4.6%, and range 78–100%. In this setting, random confidence embedding outperforms both answer-only and overall logprob-based extraction, and the authors attribute the failure to overconfidence induced by extended reasoning traces (Li et al., 13 Feb 2026).

The failure analysis is not merely quantitative. The same study identifies five failure modes—Self-Doubt, Social Conformity, Suggestion Hijacking, Emotional Susceptibility, and Reasoning Fatigue—with Self-Doubt and Social Conformity accounting for about 50% of failures (Li et al., 13 Feb 2026). This directly challenges a common assumption that any confidence-conditioned loop will stabilize behavior. In these models, the confidence variable itself is weakly discriminative, so conditioning on it can amplify a flawed signal rather than supply protection.

6. Calibration, abstention, and broader reliability infrastructure

Several later papers move from confidence-guided generation toward confidence calibration and selective action. ORCE argues that verbalized confidence should be decoupled from answer generation and aligned to correctness likelihood through order-aware training. On MMLU, ORCE reduces ECE from 0.170 to 0.025 for Llama-3 8B while preserving accuracy at 0.657, and from 0.212 to 0.034 for Qwen3 8B while preserving accuracy at 0.749. Across MMLU, DROP, and ReClor, it improves calibration and failure prediction while largely preserving answer accuracy (Li et al., 12 May 2026).

Label-Confidence-Aware uncertainty estimation attacks a different weakness: entropy-based uncertainty can be biased when the greedy-decoded label is misaligned with the sample distribution. It defines

$k$1

where $k$2 is a Gibbs probability derived from samples and $k$3 is the greedy-label confidence. Across several datasets and LLMs, this bridging improves AUROC for multiple baselines, including LNPE from 0.6568 to 0.7874 and Semantic Entropy from 0.6711 to 0.7690 (Lin et al., 2024). This suggests that CARG increasingly depends on explicit reconciliation between model sampling behavior and the source of the reported label.

Abstention-oriented CARG is especially prominent in RAG. An activation-based uncertainty model trains a lightweight sequence classifier over layer activations from answer tokens and uses a confidence threshold $k$4 to decide whether to display or withhold a response. On Llama 3.1 8B, the activation-based model with calibration reaches AUROC 0.772 versus 0.663 for a logits-based uncertainty model; at $k$5, precision is 0.95 with a 29.9% mask rate (Huang et al., 15 Oct 2025). A related pre-generation routing system combines semantic alignment, internal convergence, and learned confidence into a unified confidence score and routes queries to local generation, RAG, larger models, or human review. On knowledge-intensive QA benchmarks, that system reports hallucination detection 0.74 versus 0.42 for a baseline, F1 0.82 versus 0.61, false positive rate 0.09, and cost 1.6x compared with 4.2x for SelfCheckGPT (M, 23 Sep 2025).

The limitations reported across the literature are unusually consistent. Token-logprob confidence may track syntactic predictability of a standardized span more than semantic certainty (Li et al., 28 Mar 2025). Confidence proxies can be misestimated by entailment-judge errors or by adversarial and noisy documents that spuriously increase answer agreement (Song et al., 6 May 2026). Hidden-state and activation probes may drift under model updates or domain shift, and verbalized confidence can be prompt-sensitive or parsing-brittle (Li et al., 12 May 2026, Huang et al., 15 Oct 2025). For reasoning models, chain-of-thought can drive confidence into a narrow, overconfident range that destroys discriminative power (Li et al., 13 Feb 2026). A plausible synthesis is that the main open problem in CARG is no longer whether confidence can be used at inference time, but which confidence signal remains informative after the model architecture, decoding regime, and evidence pipeline have changed.