arxiv_cl 95% Match Research Paper AI Safety Researchers,ML Engineers,Developers of LLM applications,Researchers in Finance and Healthcare AI 2 weeks ago

Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation

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📄 Abstract

Abstract: We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs. Confidence estimation is especially critical in high-stakes domains such as finance and healthcare, where the cost of an incorrect answer outweighs that of not answering the question. Our approach extends prior uncertainty quantification methods by leveraging raw feed-forward network (FFN) activations as auto-regressive signals, avoiding the information loss inherent in token logits and probabilities after projection and softmax normalization. We model confidence prediction as a sequence classification task, and regularize training with a Huber loss term to improve robustness against noisy supervision. Applied in a real-world financial industry customer-support setting with complex knowledge bases, our method outperforms strong baselines and maintains high accuracy under strict latency constraints. Experiments on Llama 3.1 8B model show that using activations from only the 16th layer preserves accuracy while reducing response latency. Our results demonstrate that activation-based confidence modeling offers a scalable, architecture-aware path toward trustworthy RAG deployment.

Key Contributions

Proposes a novel confidence estimation method for RAG systems using raw FFN activations, which aligns better with LLM output correctness than traditional methods. It models confidence as a sequence classification task with Huber loss regularization, enabling response abstinence and outperforming baselines in a real-world financial setting under strict latency constraints.

Business Value

Enhances the safety and reliability of LLMs in critical applications like finance and healthcare by enabling them to abstain from answering when uncertain, preventing potentially harmful incorrect responses. This builds user trust and reduces risk.

Paper Metadata

Innovation Type

New Method/Algorithm

Deployment Feasibility

Moderate to High. The method is designed to work within existing RAG systems and is optimized for low latency, making it suitable for real-time applications.

Limitations Addressed

Information loss in token logits/probabilities,Need for reliable confidence estimation in high-stakes domains,Performance degradation under strict latency constraints

Performance Gains

Outperforms strong baselines,Maintains high accuracy under strict latency constraints

Technical Tags

confidence estimationLLM trustworthinessretrieval-augmented generation (RAG)uncertainty quantificationfeed-forward network activationssequence classificationHuber losshigh-stakes domainslatency constraints

Research Topics

AI SafetyLLM ReliabilityUncertainty QuantificationTrustworthy AIDomain Adaptation

Methods & Architectures

Leveraging raw FFN activationsSequence classification for confidence predictionHuber loss regularizationResponse abstinence (abstaining from answering) Large Language Models (LLMs)Retrieval-Augmented Generation (RAG) systems

Applications & Tasks

Finance Healthcare Customer Support High-Stakes AI Applications LLM IncorrectnessHigh Cost of ErrorsInformation Loss in Logits/ProbabilitiesNoisy Supervision Improving LLM TrustworthinessConfidence Estimation in RAGResponse AbstinenceUncertainty Quantification

Related Fields

Machine LearningNatural Language ProcessingAI SafetyUncertainty QuantificationDeep Learning

Keywords

LLMconfidence estimationRAGtrustworthinessuncertaintyFFN activationsfinancehealthcarelatencyresponse abstinence

Academic Context

#AI Safety#LLM Reliability#Uncertainty Quantification#Trustworthy AI#Domain Adaptation

Commercial Potential

Potential Products

LLM confidence scoring modulesTrustworthy AI components for RAG systems

Target Industries

FinanceHealthcareInsuranceLegalCustomer Service

Use Case Examples

Financial advice chatbots that abstain from uncertain recommendationsMedical diagnostic assistants that flag low-confidence assessmentsCustomer support bots that escalate complex queries

Competitive Edge

Offers a more effective and efficient method for confidence estimation in RAG systems by directly using FFN activations, overcoming limitations of logit-based methods and achieving high accuracy under strict latency requirements.

Market Opportunity

Significant market for improving LLM safety and reliability, especially in regulated industries.

Revenue Models

Licensing of the confidence estimation moduleintegration services.

Resource Requirements

Compute Needs

Moderate, requires FFN activation access during inference.

Data Requirements

Requires labeled data for training the confidence prediction model, ideally from the target domain (e.g., financial customer support).

Deployment Constraints

Integration into existing RAG pipelines, ensuring minimal latency impact.

Scalability

The method is designed to be scalable as it operates on internal model activations.

Regulatory Considerations

Highespecially for finance and healthcarewhere accuracy and reliability are regulated.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years for integration into production systems.

Patent Potential

Moderate, for the novel activation-based confidence estimation technique.

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