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[2602.15391] Improving LLM Reliability through Hybrid Abstention and Adaptive Detection
Summary
The paper presents a novel adaptive abstention system for Large Language Models (LLMs) that balances safety and utility by dynamically adjusting safety thresholds based on contextual signals, improving reliability and user experience.
Why It Matters
As LLMs become increasingly integrated into various applications, ensuring their reliability while minimizing harmful outputs is crucial. This research addresses the safety-utility trade-off, offering a scalable solution that enhances both performance and user trust in AI systems.
Key Takeaways
- Introduces an adaptive abstention system for LLMs that adjusts safety thresholds dynamically.
- Utilizes a multi-dimensional detection architecture to optimize speed and precision.
- Demonstrates significant reductions in false positives, particularly in sensitive domains.
- Achieves substantial latency improvements compared to traditional guardrail systems.
- Balances safety and utility, providing a scalable solution for reliable LLM deployment.
Computer Science > Artificial Intelligence arXiv:2602.15391 (cs) [Submitted on 17 Feb 2026] Title:Improving LLM Reliability through Hybrid Abstention and Adaptive Detection Authors:Ankit Sharma, Nachiket Tapas, Jyotiprakash Patra View a PDF of the paper titled Improving LLM Reliability through Hybrid Abstention and Adaptive Detection, by Ankit Sharma and 1 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off either a strict filtering mechanisms prevent harmful outputs but often block benign queries or a relaxed controls risk unsafe content generation. Conventional guardrails based on static rules or fixed confidence thresholds are typically context-insensitive and computationally expensive, resulting in high latency and degraded user experience. To address these limitations, we introduce an adaptive abstention system that dynamically adjusts safety thresholds based on real-time contextual signals such as domain and user history. The proposed framework integrates a multi-dimensional detection architecture composed of five parallel detectors, combined through a hierarchical cascade mechanism to optimize both speed and precision. The cascade design reduces unnecessary computation by progressively filtering queries, achieving substantial latency improvements compared to non-cascaded models and external guardrail systems. Extensive evaluation on mixed and domain-specific ...
