arXiv:2603.05881v2 Announce Type: replace Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after generating an answer, which measure the correctness of a specific response and limits practical usability. We study a confidence-first paradigm, where the model outputs its confidence before answering, interpreting this score as the model's probability of answering the question correctly under its current policy. We propose CoCA(Co-optimized Confidence and Answers), a GR
The increasing deployment of LLMs requires robust methods for uncertainty estimation to ensure safe and reliable operation, pushing innovation in this area.
Accurate and proactive uncertainty estimation for LLMs significantly enhances their reliability and trustworthiness, enabling broader and more critical applications in real-world scenarios.
LLMs can now potentially self-assess their confidence before generating an answer, shifting from post-hoc correction to pre-emptive risk mitigation.
- · LLM developers
- · AI safety researchers
- · Industries deploying LLMs in critical applications
- · Users of AI systems
- · AI systems with poor or no uncertainty estimation
- · Applications relying solely on 'answer-first' confidence metrics
LLMs become more reliable and trustworthy in sensitive applications, reducing human oversight requirements for simpler tasks.
Increased adoption of LLMs in high-stakes domains like healthcare, finance, and legal services due to enhanced safety and accountability.
Public trust in AI systems generally improves, accelerating the integration of advanced AI into daily life and critical infrastructure.
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Read at arXiv cs.CL