arXiv:2605.25226v1 Announce Type: new Abstract: Large language models are widely deployed in high-stakes NLP tasks, yet risks such as bias, hallucination, adversarial vulnerability and unreliable generalization remain. Probe-based auditing reveals inconsistencies in model behavior. Adversarial text generation uncovers robustness gaps, especially in lower-resourced languages with limited benchmarks. Enterprise text-to-SQL settings expose the difficulty of validating outputs over private and large-scale databases. Human supervision is essential for probe validation, adversarial verification and
The increasing deployment of large language models in critical applications forces a re-evaluation of their safety and trustworthiness, highlighting current limitations in auditing and validation.
Ensuring the safety and reliability of AI models is paramount for their continued adoption and integration into high-stakes workflows, directly impacting their commercial viability and public trust.
The focus is shifting from purely automated large language model development to integrating human oversight and collaborative methods for validation and risk mitigation.
- · AI safety researchers
- · Human-AI interaction specialists
- · NLP auditing companies
- · Ethical AI frameworks
- · Purely black-box AI development
- · Unvalidated AI deployments
- · Models with unaddressed bias and hallucination
- · Lower-resourced languages in NLP
Demand for specialized human-in-the-loop services and tooling for AI model validation will increase.
New standards and regulations for AI safety and trustworthiness will emerge, requiring demonstrable human oversight in high-stakes applications.
The development trajectory of AI might prioritize explainability and control over raw performance, fostering a more symbiotic human-AI ecosystem.
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Read at arXiv cs.CL