Towards a compositional semantics for quantitative confidence assessment in assurance arguments
- URL: http://arxiv.org/abs/2605.22213v1
- Date: Thu, 21 May 2026 09:17:27 GMT
- Title: Towards a compositional semantics for quantitative confidence assessment in assurance arguments
- Abstract summary: Existing approaches address structure and soundness but largely reason over truth values, not over confidence in the justification of claims.<n>We propose a confidence semantics that represents argument elements as SL opinions and maps relations between elements to SL operators modelling how confidence flows.
- Score: 0.10499611180329803
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Assurance arguments provide a clear and structured way to explain why stakeholders should trust that a system satisfies certain properties, yet widely used notations, e.g.Goal Structuring Notation (GSN), typically lack an operational semantics for deriving assurance confidence. Existing approaches address structure and soundness but largely reason over truth values, not over confidence in the justification of claims. Subjective Logic (SL) offers a calculus of belief, disbelief, and uncertainty with operators for combining opinions, enabling confidence propagation under incomplete, conflicting, or subjective evidence. However, existing SL-based approaches do not provide a uniform, compositional semantics that covers all argument elements and relations to enable overall confidence assessment. We propose a confidence semantics that represents argument elements as SL opinions and maps relations between elements to SL operators modelling how confidence flows, effectively turning the argument into an analyzable confidence network. The approach provides explicit warrants, principled handling of context, preserved provenance, and compatibility with GSN, along with practical guidance using an exemplary assurance confidence assessment.
Related papers
- IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals [2.623527845999313]
We argue that reliable factuality control can be achieved through introspective signals derived from the model itself.<n>IntroConformal is a training-free Conformal Risk Control framework that provides finite-sample, distribution-free factuality guarantees.
arXiv Detail & Related papers (2026-09-01T15:09:56Z) - Confidence-Aware Alignment Makes Reasoning LLMs More Reliable [65.44962502963378]
We introduce CASPO, a framework that aligns token-level confidence with step-wise logical correctness without training a separate reward model.<n>During inference, we propose Confidence-aware Thought (CaT), which dynamically prunes uncertain reasoning branches with negligible O(V) latency.<n>Experiments across ten benchmarks and multiple model families show that CASPO consistently improves reasoning reliability and inference efficiency.
arXiv Detail & Related papers (2026-05-08T07:08:25Z) - FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning [49.65751420291115]
Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps.<n>We propose FACT-E, a causality-inspired framework for evaluating CoT quality.<n>We show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learnings.
arXiv Detail & Related papers (2026-04-12T15:35:08Z) - ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation [19.616905640490252]
We propose a new framework called ERA (Evidence-based Reliability Alignment) to enhance abstention behavior in RAG systems.<n>Our approach significantly outperforms baselines, optimizing the trade-off between answer coverage and abstention with superior calibration.
arXiv Detail & Related papers (2026-02-24T01:45:51Z) - Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency [78.91846841708586]
We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference.<n>We propose Neighbor-Consistency Belief (NCB), a structural measure of belief that evaluates response coherence across a conceptual neighborhood.<n>We also present Structure-Aware Training (SAT), which optimize context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%.
arXiv Detail & Related papers (2026-01-09T16:23:21Z) - PaTAS: A Parallel System for Trust Propagation in Neural Networks Using Subjective Logic [1.8485970721272895]
This paper introduces the Parallel Trust Assessment System (PaTAS), a framework for modeling and propagating trust in neural networks.<n>Experiments on real-world and adversarial datasets demonstrate that PaTAS produces interpretable, symmetric, and convergent trust estimates.
arXiv Detail & Related papers (2025-11-25T18:15:36Z) - Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation [63.49409574310576]
Large language models (LLMs) exhibit overconfidence, assigning high confidence scores to incorrect predictions.<n>We introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation.<n>Our code and all baselines used in the paper are available on GitHub.
arXiv Detail & Related papers (2025-08-16T13:29:35Z) - Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMs [2.4892313127400962]
We study the source of uncertainty in DeepSeek R1-32B by analyzing its self-reported verbal confidence on question answering (QA) tasks.<n>We show that granting DeepSeek the budget to explore its distribution by forcing a long chain-of-thought before the final answer greatly improves its verbal score effectiveness.
arXiv Detail & Related papers (2025-05-28T17:01:30Z) - Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks.<n>They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct.<n>We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
arXiv Detail & Related papers (2025-01-02T16:38:21Z) - When to Trust LLMs: Aligning Confidence with Response Quality [49.371218210305656]
We propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD)
It integrates quality reward and order-preserving alignment reward functions.
Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy.
arXiv Detail & Related papers (2024-04-26T09:42:46Z) - TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness [58.721012475577716]
Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications.
This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
arXiv Detail & Related papers (2024-02-19T21:12:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.