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Model Reliability

Model reliability encompasses the evaluation, prediction, and mitigation of failure modes in neural systems deployed in practice. Reliable models must generalize beyond training data, handle distribution shift and adversarial inputs, maintain consistent performance across diverse conditions, and behave transparently when uncertain. Unreliable systems generate confident errors, hallucinate, exhibit hidden biases, or fail catastrophically on edge cases.

For machine translation, neural language generation, and other high-stakes applications, reliability failures can propagate downstream. Hallucinations, undertranslation, toxic outputs, and domain shift all compromise the trustworthiness of deployed systems. Understanding and mitigating these failure modes is essential for safe, robust NLP systems.

Key papers

  • [[2023-guerreiro-hallucinations-multilingual]] — identifies and characterizes hallucinations in multilingual translation models across diverse language pairs and resource levels