Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
Seongjun Lee, Suwan Yoon, Changhee Lee
Abstract
As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a critical requirement for safety and trust. However, current uncertainty quantification methods primarily operate at the output level, often failing to distinguish whether uncertainty arises from the model's lack of knowledge or from ambiguity in the user's input. While input-centric uncertainty quantification has recently emerged as a promising direction, it remains relatively underexplored and typically relies on coarse, input-level information. Consequently, users are provided with scalar uncertainty scores that offer little actionable guidance on which parts of the input should be clarified to improve reliability. To address this limitation, we propose Shapley-based input uncertainty Quantification (ShaQ), a framework for span-level attribution of input-induced uncertainty. Our approach models ambiguous spans in the input as players in a cooperative game and quantifies their contributions using Shapley values, defined via the weighted average of marginal reductions in conditional entropy obtained by clarifying each span coalition. Unlike existing input-level approaches, our formulation captures complex interactions among spans and provides a principled decomposition in which individual attributions sum exactly to the total input-induced uncertainty. We evaluate ShaQ on the AmbigQA and AmbiEnt benchmarks, where it achieves state-of-the-art performance in ambiguity detection. We further demonstrate its utility on MediTOD, showing that ShaQ can localize under-specified clinical utterances and facilitate human-AI collaboration in high-stakes settings. Overall, ShaQ improves uncertainty estimation and provides actionable insights for targeted input clarification.
AI Impact Assessments
(1 models)Scientific Impact Assessment: ShaQ — Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values
1. Core Contribution
ShaQ addresses a genuine and underexplored gap in LLM uncertainty quantification: while existing methods provide scalar uncertainty scores (either output-level or input-level), they fail to pinpoint *which specific spans* in the input drive that uncertainty. The paper formulates ambiguity localization as a cooperative game where ambiguous spans are players, and uses Shapley values to fairly decompose the total input-induced (aleatoric) uncertainty into per-span attributions. The key insight is that ambiguous spans can interact—clarifying one span may implicitly resolve ambiguity in another—and the Shapley formulation naturally handles these dependencies by averaging marginal contributions across all coalitions.
The framework generalizes the prior Input Clarification Ensemble (ICE) method of Hou et al. (2024): the global aleatoric uncertainty from ICE is recovered as the sum of span-level Shapley values via the efficiency axiom. This theoretical relationship is clean and establishes ShaQ as a strict extension rather than an alternative paradigm.
2. Methodological Rigor
Theoretical foundations are solid. The value function is well-defined via mutual information between the output and span-level clarifications (Theorem 4.1). The efficiency property (Remark 4.1) guarantees exact decomposition. The bottom-up marginalization algorithm (Algorithm 1) is a practical contribution that ensures hierarchical monotonicity (Property 4.1), guaranteeing non-negative marginal contributions—a critical property since naive independent estimation could produce negative information gains, undermining interpretability.
Experimental design is reasonable but has limitations. The evaluation spans three benchmarks: AmbigQA (open-domain QA), AmbiEnt (NLI), and MediTOD (clinical dialogue). Multiple LLM backbones are tested (GPT-4, GPT-5.4-mini, Gemini variants), lending credibility to robustness claims. The comparison includes both output-level methods (Semantic Entropy, Sample Diversity) and aleatoric-specific methods (ICE, Deep Ensembles, Ask4Conf-D).
However, several methodological concerns arise:
3. Potential Impact
The paper addresses a practical need: when an LLM signals high uncertainty, users need to know *what to fix*. The uncertainty-guided clarification experiment (Table 5) demonstrates that ShaQ achieves higher entropy reduction with fewer edits than baselines—a compelling result for interactive LLM systems. The MediTOD qualitative analysis, while not quantitatively evaluated, illustrates a compelling use case: real-time ambiguity monitoring in clinical dialogues.
Real-world applicability is promising but constrained by computational cost. Each input requires multiple LLM calls for localization, premise generation, answer sampling across all coalitions, and clustering. Even with KV-cache optimization, this is substantially more expensive than single-pass uncertainty estimation.
The framework could influence: (1) interactive AI assistants that proactively request clarification of specific spans, (2) clinical NLP systems requiring fine-grained ambiguity detection, and (3) the broader interpretability community by extending Shapley-based attribution from prediction explanation to uncertainty explanation.
4. Timeliness & Relevance
This work is highly timely. As LLMs are deployed in high-stakes domains, the distinction between model uncertainty (hallucination) and input uncertainty (ambiguity) becomes crucial. The recent ICE paper (ICML 2024) established input-level uncertainty as a viable direction; ShaQ is a natural and principled extension that adds localization. The paper appears among the first to formally connect Shapley values, cooperative game theory, and LLM input uncertainty quantification.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
Additional Observation: The paper's improvements on AmbigQA with GPT-4 are more modest (AUROC ~66% vs ~61% for ICE) than on AmbiEnt with GPT-5.4-mini (AUROC ~78% vs ~55%), suggesting performance may be sensitive to the alignment between the ambiguity structure and the backbone model's capabilities.
Summary
ShaQ makes a meaningful conceptual contribution by bridging cooperative game theory and input uncertainty localization for LLMs. The theoretical framework is elegant and the empirical results are encouraging, particularly for compositional ambiguity detection. However, the inability to quantitatively evaluate span-level localization, exponential scaling, and computational overhead temper the immediate practical impact. This work opens a promising research direction, but its full potential will depend on addressing scalability and developing proper evaluation protocols for fine-grained uncertainty attribution.
Generated May 28, 2026
Comparison History (19)
Paper 1 proposes a fundamentally novel framework for tracing the lineage of AI-generated content through steganographic inheritance—a paradigm-shifting concept addressing the critical and timely problem of synthetic content provenance. Its biological evolution analogy introduces a new conceptual vocabulary for information tracing, with broad implications across AI safety, intellectual property, misinformation detection, and digital forensics. While Paper 2 makes a solid contribution to LLM uncertainty quantification via Shapley values, it is more incremental, building on existing UQ and attribution methods. Paper 1's broader societal relevance and cross-disciplinary novelty give it higher potential impact.
Paper 2 addresses a critical bottleneck in deploying LLMs in high-stakes domains by providing an actionable, span-level uncertainty quantification method. Using Shapley values to localize input ambiguity is highly novel and offers immediate real-world utility, particularly in safety-critical areas like healthcare. While Paper 1 provides valuable empirical insights into MLLM explainability, Paper 2 proposes a concrete algorithmic framework that directly improves human-AI collaboration and model reliability, granting it broader practical and interdisciplinary impact.
Paper 2 has broader and more immediate cross-domain impact: span-level, Shapley-theoretic decomposition of input-induced uncertainty is a generally applicable tool for safer LLM deployment in many high-stakes settings (health, law, customer support). It is methodologically principled (entropy-based uncertainty, exact additive attributions, interaction-aware via Shapley values) and evaluated across multiple established benchmarks plus a clinical dialogue setting, supporting rigor and relevance. Paper 1 is strong and novel for materials synthesis reasoning, but its impact is narrower to materials process domains and depends on adoption of a specific benchmark/graph extraction pipeline.
Paper 1 addresses a highly timely and critical challenge in deploying Large Language Models: input-induced uncertainty in high-stakes settings. By introducing a principled, Shapley-based framework for span-level attribution, it provides actionable insights for prompt clarification, advancing AI safety and human-AI collaboration. Paper 2 offers a strong technical solution for modality balancing in Multimodal Sentiment Analysis, but its scope and potential applications are significantly narrower compared to the broad, cross-disciplinary impact of improving LLM reliability and trust.
Paper 2 has higher potential impact due to timeliness and broad applicability in rapidly growing text-to-image alignment and RLHF/RLAIF workflows. AutoRubric-T2I offers a novel, data-efficient alternative to large BT-trained reward models, emphasizing interpretability and drastic reduction in human preference data (0.01%), with demonstrated downstream RL benefits—making it attractive for real-world deployment and scalable adaptation. Paper 1 is methodologically principled and useful for safety-critical LLM use, but its impact is narrower (input ambiguity localization) and may face higher computational/operational overhead from Shapley-style attribution.
Paper 2 addresses a fundamental, highly debated question regarding LLM capabilities (introspection and metacognition). By rigorously debunking flawed evaluation paradigms and introducing better controls, it prevents the field from pursuing scientifically unsound directions. This foundational 'reality check' is likely to broadly influence how researchers evaluate and interpret LLM internal states, generating broader theoretical impact than Paper 1's specific (though practically useful) methodological contribution to uncertainty quantification.
Paper 1 likely has higher scientific impact: it introduces a principled, game-theoretic method (Shapley values) to localize input-induced uncertainty in LLMs with additive guarantees, addressing a timely safety/trust gap and enabling actionable input clarification. It’s broadly applicable across LLM deployments (search, agents, clinical NLP) and connects to uncertainty, interpretability, and human-AI interaction, with evaluation on established benchmarks plus a high-stakes clinical setting. Paper 2 is important for AI ethics, but its contribution is more domain-specific and framed as supervised classification on a relatively small benchmark, limiting generality and rigor compared to Paper 1’s transferable methodology.
Paper 2 addresses the critical issue of LLM safety and reliability in high-stakes decision-making through a rigorous, Shapley-based uncertainty quantification framework. By providing actionable, span-level attribution of input ambiguity rather than coarse output scores, it offers immediate practical value for human-AI collaboration in fields like healthcare. While Paper 1 offers a solid framework for resource-constrained agents, Paper 2's focus on explainable safety and trust has broader cross-disciplinary implications and higher potential for widespread real-world impact.
Paper 2 likely has higher scientific impact due to greater novelty and breadth: it bridges mechanistic interpretability (circuit tracing) with practical CoT faithfulness detection via a scalable, instance-level internal–external discrepancy metric (FGW distance). This connects to pressing concerns about LLM reliability and evaluation, with applications across domains using CoT. Paper 1 is strong and useful (span-level input ambiguity attribution via Shapley values) but is a more incremental extension of established attribution concepts and is narrower in scope (input ambiguity/UQ) compared to broad relevance of CoT faithfulness and interpretability-informed auditing.
Paper 1 (ShaQ) addresses a fundamental and underexplored problem—localizing input uncertainty in LLMs using Shapley values—with strong theoretical grounding and broad applicability to high-stakes domains like clinical AI. It introduces a principled framework with exact decomposition properties and demonstrates utility across multiple benchmarks including safety-critical medical settings. Paper 2 (DREAM-R) offers engineering improvements for speculative reasoning speed, but is more incremental in nature, focusing on efficiency optimization rather than opening a new research direction. ShaQ's novelty in connecting game theory to input uncertainty and its potential for human-AI collaboration give it broader and deeper impact.
Paper 1 addresses a critical bottleneck in LLM development: the high computational cost of improving reasoning capabilities. By introducing a highly efficient, non-parametric method (CORE) that requires significantly fewer samples and rollouts, it offers a scalable and interpretable solution for model self-improvement. This has widespread applicability across AI development, potentially fundamentally shifting how models are optimized for complex tasks, giving it a broader scientific and practical impact than the targeted uncertainty quantification approach in Paper 2.
Paper 2 offers a concrete, technically novel and well-scoped method (Shapley-based span-level decomposition of input-induced uncertainty) with clear evaluation on established benchmarks and demonstrated utility in clinical dialogue, aligning with urgent LLM safety needs. Its methodological rigor (formal decomposition, measurable objectives, empirical SOTA claims) and immediate applicability across many LLM deployments suggest strong near-term impact. Paper 1 is broad and potentially influential conceptually, but appears more programmatic/theoretical with less clearly specified causal identification and validation, making its impact harder to realize and assess.
Paper 1 addresses a critical and highly timely issue—LLM safety and uncertainty quantification. By providing actionable, span-level uncertainty localization using Shapley values, it has immediate, broad applications in high-stakes fields like healthcare. Paper 2 presents a solid methodological improvement in multi-agent reinforcement learning, but its impact is mostly confined to the specific subfield of game theory and equilibrium computation, whereas Paper 1 intersects with the rapidly expanding and universally relevant domain of LLM reliability.
Paper 2 (ShaQ) addresses a fundamental and timely problem—localizing input uncertainty in LLMs using a principled Shapley value framework. It offers clear novelty (span-level attribution of input uncertainty), strong methodological rigor (game-theoretic foundations, exact decomposition property), broad applicability (clinical AI, QA systems, any high-stakes LLM deployment), and state-of-the-art results on established benchmarks. Paper 1, while technically detailed, is primarily a system/protocol specification for AI-assisted research workflows with self-referential evaluation, offering less generalizable scientific contributions and narrower impact potential.
Paper 2 presents a rigorous, novel framework (ShaQ) for localizing input uncertainty in LLMs using Shapley values, with clear methodological contributions, formal theoretical grounding, state-of-the-art benchmark results, and practical applications in high-stakes domains like clinical AI. Paper 1, while exploring an interesting topic, relies on auto-ethnographic methodology with a single human-AI dyad, lacks reproducibility, makes epistemically tenuous claims about AI phenomenology, and conflates observed behavioral patterns with unverifiable first-person AI self-reports, significantly limiting its scientific rigor and broader impact.
Paper 2 has higher potential impact due to broader applicability and stronger real-world leverage: a decentralized multi-agent framework for long-running scientific experimentation can generalize across many domains and directly accelerates research workflows. It reports large, budget-matched improvements on diverse, high-stakes benchmarks (BioML-Bench, GPT training optimization, ProteinGym) suggesting methodological effectiveness and scalability. Paper 1 is novel and useful for safety/clarification in LLM use, but its impact is narrower (input ambiguity attribution) and more specialized to LLM uncertainty tooling rather than a general engine for scientific discovery.
Paper 2 is more novel and broadly impactful: it introduces a principled, span-level input-induced uncertainty decomposition using Shapley values with exact additivity, enabling actionable clarification guidance. This directly targets safety/trust in high-stakes LLM use (timely, high real-world relevance) and is applicable across domains (clinical, QA, dialogue, decision support). The methodology is grounded in information theory and cooperative game theory and is evaluated on multiple benchmarks plus a high-stakes setting. Paper 1 is practically useful for agent robustness, but the idea of training with noise/curricula is more incremental and less generalizable across fields.
Paper 1 likely has higher scientific impact: it addresses a timely, central AI control/safety failure mode (information leakage via retrying under adversarial models) and proposes/ablates resampling-based alternatives with concrete, budgeted safety–utility tradeoffs. The work directly informs deployment of agentic coding systems and revises conclusions from prior literature (e.g., Ctrl-Z), suggesting field-shaping potential. Methodologically, it uses realistic agent benchmarks and explicit threat modeling. Paper 2 is solid and useful, but Shapley-based span attribution for uncertainty is a more incremental extension of existing attribution/UQ ideas with narrower cross-field impact.
Paper 1 (ShaQ) addresses a fundamental gap in LLM uncertainty quantification by providing span-level attribution of input-induced uncertainty using Shapley values—a principled game-theoretic approach. It tackles a critical need for trustworthy AI in high-stakes settings (e.g., clinical applications), offers a novel decomposition framework with exact attribution guarantees, and demonstrates broad applicability across multiple benchmarks. Paper 2 contributes a useful portfolio approach for optimization model generation but addresses a narrower problem. Paper 1's contribution to interpretable uncertainty quantification has broader cross-domain impact and addresses a more fundamental challenge in AI safety and trust.