From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP
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 domain-specific annotation, but it is costly and hard to scale. This survey examines recent human-in-the-loop methods that shift NLP from automat...
| Authors | Most. Sharmin Sultana Samu, MD. Tanvir Ahmed Seum, Md. Rakibul Islam |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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