Reliability Testing for Natural Language Processing Systems

• URL: http://arxiv.org/abs/2105.02590v1
• Date: Thu, 6 May 2021 11:24:58 GMT
• Title: Reliability Testing for Natural Language Processing Systems
• Abstract summary: We argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests.
• Score: 14.393308846231083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing -- with an emphasis on interdisciplinary collaboration -- will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.

Related papers

• Consilience for Verifier-Free Test-Time Scaling [17.772780906204787]
  Verifier-free test-time scaling (or VF-TTS) is gaining extensive attention as a mechanism to enhance Large Language Model (LLM) reasoning.<n>We introduce consilience, a novel selection framework that explicitly evaluates the temporal asymmetry of confidence in reasoning.
  arXiv  Detail & Related papers  (2026-08-10T17:45:44Z)
• Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
  Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
  arXiv  Detail & Related papers  (2026-07-28T17:50:44Z)
• Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification [38.209854965937495]
  Embodied AI systems are increasingly deployed in open-world environments.<n>This article argues that reliability in embodied AI is inherently a lifecycle assurance problem.
  arXiv  Detail & Related papers  (2026-06-02T12:58:40Z)
• Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? [50.17126900886782]
  It remains unclear whether models can apply their own linguistic confidence framework to associate markers with specific confidence levels in a stable and generalizable way.<n>We formalize _marker internal confidence_ (MIC) as the estimated intrinsic confidence a model associates with a specific epistemic marker in a given task domain.<n>Applying our analysis framework to diverse models and tasks, we find that LLMs remain faithfully miscalibrated even under model-centric interpretation of marker meanings.
  arXiv  Detail & Related papers  (2026-05-27T17:38:00Z)
• Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
  Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
  arXiv  Detail & Related papers  (2026-05-17T10:26:37Z)
• Less Approximates More: Harmonizing Performance and Confidence Faithfulness via Hybrid Post-Training for High-Stakes Tasks [52.81286869496811]
  Large language models are increasingly deployed in high-stakes tasks, where confident yet incorrect inferences may cause real-world harm.<n>We propose to jointly optimize unsupervised Reinforcement Learning from Internal Feedback with reasoning-trace-guided Reasoning Distillation.<n>We introduce HyTuning, a hybrid post-training framework that adaptively reweights RD and RLIF via a PRG-style metric.
  arXiv  Detail & Related papers  (2026-04-09T16:50:11Z)
• Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations [49.84786015324238]
  Confidence estimation (CE) indicates how reliable the answers of large language models (LLMs) are, and can impact user trust and decision-making.<n>We present a comprehensive evaluation framework for CE that measures their confidence quality on three new aspects.<n>These include robustness of confidence against prompt perturbations, stability across semantic equivalent answers, and sensitivity to semantically different answers.
  arXiv  Detail & Related papers  (2026-01-12T23:16:50Z)
• 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)
• Adaptive Trust Metrics for Multi-LLM Systems: Enhancing Reliability in Regulated Industries [0.0]
  Large Language Models (LLMs) are increasingly deployed in sensitive domains such as healthcare, finance, and law.<n>This paper explores adaptive trust metrics for multi LLM ecosystems.
  arXiv  Detail & Related papers  (2026-01-07T01:50:10Z)
• Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems [54.916243942641444]
  Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications.<n>We study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline.
  arXiv  Detail & Related papers  (2025-12-23T03:10:09Z)
• BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents [58.05949210993854]
  We investigate whether search agents have the ability to communicate their own confidence through verbalized confidence scores after long sequences of actions.<n>We propose Test-Time Scaling (TTS) methods that use confidence scores to determine answer quality, encourage the model to try again until reaching a satisfactory confidence level.
  arXiv  Detail & Related papers  (2025-10-27T15:58:51Z)
• FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning [62.452350134196934]
  FaithCoT-Bench is a unified benchmark for instance-level CoT unfaithfulness detection.<n>Our framework formulates unfaithfulness detection as a discriminative decision problem.<n>FaithCoT-Bench sets a solid basis for future research toward more interpretable and trustworthy reasoning in LLMs.
  arXiv  Detail & Related papers  (2025-10-05T05:16:54Z)
• Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable? [18.587739647424716]
  This paper defines a trustworthiness metric, diagnostic faithfulness, for models to gain service providers' trust.<n>We propose FaithLog, a faithful log-based anomaly detection system, which achieves faithfulness through a carefully designed causality-guided attention mechanism and adversarial consistency learning.
  arXiv  Detail & Related papers  (2025-09-29T06:52:40Z)
• Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution [20.607071807794195]
  Large Language Models (LLMs) are widely used as automated judges, where practical value depends on both accuracy and trustworthy, risk-aware judgments.<n>Existing approaches predominantly focus on accuracy, overlooking the necessity of well-calibrated confidence.<n>We advocate a shift from accuracy-centric evaluation to confidence-driven, risk-aware LLM-as-a-Judge systems.
  arXiv  Detail & Related papers  (2025-08-08T11:11:22Z)
• Attention Knows Whom to Trust: Attention-based Trust Management for LLM Multi-Agent Systems [52.57826440085856]
  Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages.<n>This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness.<n>We propose Attention Trust Score (A-Trust), a lightweight, attention-based method for evaluating message trustworthiness.
  arXiv  Detail & Related papers  (2025-06-03T07:32:57Z)
• Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems [19.59294293070619]
  We introduce semantic stability as a criterion for assessing the response consistency of model responses.<n>We develop the first stability-aware general-purpose prompt generation system.<n>Our work offers a new perspective on prompt design and contributes practical tools for building more trustworthy general-purpose systems.
  arXiv  Detail & Related papers  (2025-05-19T03:28:33Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts. Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models [91.24296813969003]
  This paper advocates integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML. We argue that a causal approach is essential for balancing multiple competing objectives in both trustworthy ML and foundation models.
  arXiv  Detail & Related papers  (2025-02-28T14:57:33Z)
• MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels [16.300463494913593]
  Large Language Models (LLMs) require robust confidence estimation. McQCA-Eval is an evaluation framework for assessing confidence measures in Natural Language Generation.
  arXiv  Detail & Related papers  (2025-02-20T05:09:29Z)
• Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
  Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
  arXiv  Detail & Related papers  (2025-01-02T16:38:21Z)
• FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows" [74.7488607599921]
  FaithEval is a benchmark to evaluate the faithfulness of large language models (LLMs) in contextual scenarios. FaithEval comprises 4.9K high-quality problems in total, validated through a rigorous four-stage context construction and validation framework.
  arXiv  Detail & Related papers  (2024-09-30T06:27:53Z)
• Trustworthiness for an Ultra-Wideband Localization Service [2.4979362117484714]
  This paper proposes a holistic trustworthiness assessment framework for ultra-wideband self-localization. Our goal is to provide guidance for evaluating a system's trustworthiness based on objective evidence. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats.
  arXiv  Detail & Related papers  (2024-08-10T11:57:10Z)
• Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models [14.5291643644017]
  We introduce the concept of Confidence-Probability Alignment. We probe the alignment between models' internal and expressed confidence. Among the models analyzed, OpenAI's GPT-4 showed the strongest confidence-probability alignment.
  arXiv  Detail & Related papers  (2024-05-25T15:42:04Z)
• 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)
• A Holistic Assessment of the Reliability of Machine Learning Systems [30.638615396429536]
  This paper proposes a holistic assessment methodology for the reliability of machine learning (ML) systems. Our framework evaluates five key properties: in-distribution accuracy, distribution-shift robustness, adversarial robustness, calibration, and out-of-distribution detection. To provide insights into the performance of different algorithmic approaches, we identify and categorize state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-07-20T05:00:13Z)
• Did You Mean...? Confidence-based Trade-offs in Semantic Parsing [52.28988386710333]
  We show how a calibrated model can help balance common trade-offs in task-oriented parsing. We then examine how confidence scores can help optimize the trade-off between usability and safety.
  arXiv  Detail & Related papers  (2023-03-29T17:07:26Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Demonstrating Software Reliability using Possibly Correlated Tests: Insights from a Conservative Bayesian Approach [2.152298082788376]
  We formalise informal notions of "doubting" that the executions are independent. We develop techniques that reveal the extent to which independence assumptions can undermine conservatism in assessments.
  arXiv  Detail & Related papers  (2022-08-16T20:27:47Z)
• An evaluation of word-level confidence estimation for end-to-end automatic speech recognition [70.61280174637913]
  We investigate confidence estimation for end-to-end automatic speech recognition (ASR) We provide an extensive benchmark of popular confidence methods on four well-known speech datasets. Our results suggest a strong baseline can be obtained by scaling the logits by a learnt temperature.
  arXiv  Detail & Related papers  (2021-01-14T09:51:59Z)

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.