CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement

Abstract: In open-domain human-computer interaction scenarios, LLMs frequently encounter user queries that are ambiguous or incomplete. In such cases, directly producing an answer often leads to overgeneralized, erroneous, or low-information responses. In contrast, asking clarifying questions can substantially improve interaction quality. However, existing approaches still rely heavily on manually annotated data or preference alignment to address two fundamental challenges: when clarification is necessary, and which aspect of the query should be clarified. This reliance incurs high annotation costs and limits generalization. To address these challenges, we propose CLAIM, an uncertainty-driven framework for active clarification learning in open-domain settings. CLAIM eliminates the need for explicit human preference annotations by quantifying query uncertainty through the entropy induced by answer disagreements across multiple models. This uncertainty signal is then used to construct high-quality synthetic data, enabling the training of a unified clarification decision model through a combination of supervised learning and reinforcement learning. Specifically, we propose an entropy-driven synthetic data generation pipeline that integrates entropy-based uncertainty estimation with semantic clustering and reasoning-based judgments, enabling reliable automatic annotation of clarification requirements. To train CLAIM, we formulate the clarification process as a structured decision generation problem and adopt a training paradigm that combines supervised fine-tuning (SFT) with group-relative policy optimization (GRPO). Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data, offering a low-cost and robust solution for proactive understanding in real-world open-domain interactions with LLMs.

• The paper introduces CLAIM, a framework that combines cross-model semantic entropy, completeness judgments, and information-gain ranking to train clarification behavior without human preference annotations.
• CLAIM achieves 81.85% accuracy on ClariLM-test and 65.18% on CLAMBER with an 8B model trained on roughly 10,000 synthetic examples, outperforming or matching larger supervised baselines.
• Information-gain question selection substantially improves clarification quality, raising CDA from 43.05 to 55.17 and CQSS from 52.17 to 67.38, while multi-turn extension and threshold robustness remain open challenges.

CLAIM is an uncertainty-driven framework for open-domain clarification in LLMs, developed at the Gaoling School of Artificial Intelligence, Renmin University of China. The paper addresses two coupled decisions in human–LLM interaction: when a query requires clarification rather than a direct answer, and which missing information dimension a clarifying question should target. Its central claim is that both decisions can be supervised entirely by model-intrinsic signals—specifically, the entropy of the answer distribution induced by disagreement across heterogeneous LLMs—eliminating human preference annotation while achieving competitive or superior performance to prior methods that require large annotated corpora.

Motivation and positioning

The paper builds on the observation that well-specified queries elicit semantically consistent answers across different LLMs, whereas ambiguous or underspecified queries produce semantically divergent answers. This connects to prior work on semantic entropy for uncertainty estimation (Kuhn et al., 2023, Kossen et al., 2024) and self-consistency (Kadavath et al., 2022), but extends the idea from repeated sampling within a single model to cross-policy disagreement, motivated in part by policy-discriminative learning (Dou et al., 7 Jul 2025). The authors argue that existing clarification approaches (ClariLM [(2608.11631)'s related work, zhao2025clarilm], outcome-aware future-turn modeling, preference-based training) depend on costly manual supervision that does not scale to the long-tail diversity of open-domain queries. Benchmarks such as CLAMBER and QuestBench support the premise that strong generation ability does not translate into reliable clarification behavior.

A notable design decision is the separation of CLAIM-Agent—an offline, multi-model data-construction pipeline—from CLAIM, the single trained policy used at inference. The multi-model cost (up to 25 LLM calls per clarification query, roughly 5.7M tokens per 1k synthetic examples) is paid only offline; deployment requires a single forward pass.

Method

The pipeline proceeds in five stages. First, $k_1=5$ heterogeneous LLMs (DeepSeek-V3, Qwen3-32B, GLM-4-32B, Kimi-K2, Ling-flash-2.0) generate candidate answers to a query; answers are embedded with Qwen3-Embedding-8B, semantically clustered, and the cluster distribution's entropy $E_1(q)$ quantifies query uncertainty. Second, clarification judgement combines a threshold rule ($\tau = 0.45$, chosen just below the entropy of a $(0.8, 0.2)$ split under $k_1 = 5$, so that a single dissenting model triggers a weak uncertainty signal) with an independent reasoning-model judgement of semantic completeness. Third, when the two signals conflict—an event occurring in 52.42% of training samples and 27.78–46.60% of evaluation samples, a substantial proportion that justifies the arbitration step—a dedicated judgement model resolves the conflict. Fourth, for queries judged to need clarification, $k_2 = 3$ candidate clarifying questions are generated with diversity constraints, and each is scored by information gain: the reduction in answer-distribution entropy after a simulated user answer, computed by re-running the same $k_1$ models. The highest-gain question is selected. Fifth, the resulting structured supervision trains Meta-Llama-3.1-8B-Instruct via LoRA-based SFT followed by GRPO with group-relative advantages, clipped updates ($\epsilon = 0.2$), and a KL coefficient of 0.01, concentrating optimization on high-uncertainty boundary queries.

Experimental results

Evaluation covers ClariLM-test, IN3, and CLAMBER, using clarification-necessity accuracy/F1 and question-quality metrics (CDA via LLM-judged dimension match, CQSS via embedding cosine similarity). The headline comparison is against ClariLM, which is trained on approximately 120k supervised and preference-annotated examples; CLAIM achieves SOTA or near-SOTA on most metrics using roughly 10k uncertainty-constructed instances. Representative results:

Model	ClariLM-test Acc	ClariLM-test CDA	CLAMBER Acc	CLAMBER CDA
Qwen3-32B (zero-shot)	76.90	54.44	57.80	56.40
SFT-Full	79.60	55.17	61.99	62.27
ClariLM	81.25	52.50	64.23	62.89
CLAIM (8B)	81.85	56.79	65.18	63.71

Ablations support each component. Removing either judgement signal degrades accuracy (SFT-Entropy only: 74.60; SFT-LLM only: 73.40 on ClariLM-test, versus 79.60 for SFT-Full), indicating complementary signals. Removing information-gain selection preserves necessity accuracy but collapses question quality (CDA drops from 55.17 to 43.05; CQSS from 67.38 to 52.17), establishing that uncertainty-reduction ranking is the primary driver of question quality. GRPO adds further gains (accuracy 79.60 → 81.85; CLAMBER 61.99 → 65.18). Generalization is addressed by SFT-IN3, which overfits its domain (89.81 on IN3 but 56.00 on CLAMBER), whereas SFT-Full remains balanced across benchmarks—evidence that the improvements are not merely task-specific post-training artifacts. Pairwise GPT-5 judge and human evaluations (three experts plus general users, 100 instances per baseline) are directionally consistent with the automatic metrics, though the advantage over 32B-scale baselines shrinks with more ties.

Limitations and open questions

The paper concedes several constraints. The framework is single-turn only; extending to multi-turn interaction would require dialogue-state tracking, history-dependent uncertainty estimation, and planning over future turns, none of which are addressed. The global entropy threshold $\tau = 0.45$ is fixed by an argument specific to $k_1 = 5$ and is not tuned per domain, leaving its robustness at other sampling budgets unverified. The diagnostic on sampling strategies shows multi-model disagreement yields clarification ratios closer to—but still biased above—ground truth (e.g., 77.80 vs. 61.90 on ClariLM-test), while single-model sampling under-triggers (34.26 vs. 87.96 on IN3); the paper offers only a preliminary qualitative explanation for why within-model sampling remains semantically concentrated. Evaluation of question quality relies on an LLM judge for CDA and embedding similarity for CQSS, both of which are proxies for downstream interaction utility. The cost of the arbitration stage—required for over half of training samples—also means the pipeline's reliability depends on the arbitration model itself, which is not independently ablated.

Conclusion

The paper demonstrates that cross-model semantic disagreement, aggregated through clustering entropy and refined by information-gain question selection, is a sufficient supervision signal for open-domain clarification: a single 8B model trained on ~10k synthetic instances matches or exceeds a preference-trained baseline built from ~120k annotated examples. The strongest quantitative evidence concerns question quality, where information-gain selection accounts for a double-digit CDA improvement. The principal open question left by the work is whether history-dependent uncertainty estimation can extend this framework to multi-turn clarification without reintroducing manual supervision.