Why Does AI Get It Wrong?
1 min read
Despite rapid advances in machine translation (MT), errors in tone, context and terminology persist. Large language models (LLMs) often excel at single sentences but stumble over longer texts that require consistency or cultural nuance. These issues usually arise when systems lack access to broader linguistic context, struggle with ambiguity, or reflect training data limitations. The February 2025 update of the…
Despite rapid advances in machine translation (MT), errors in tone, context and terminology persist. Large language models (LLMs) often excel at single sentences but stumble over longer texts that require consistency or cultural nuance. These issues usually arise when systems lack access to broader linguistic context, struggle with ambiguity, or reflect training data limitations.
The February 2025 update of the EU AI Act urges AI developers to ensure greater transparency, particularly around model limitations and data use. This helps translators assess whether a tool is suitable for complex tasks. For example, disclosure about domain-specific training or known linguistic gaps supports more effective tool selection and editing workflows.
Human translators can mitigate these issues by providing more context, reviewing outputs carefully, and using AI as a collaborative tool rather than a replacement. By understanding where errors come from, translators are better equipped to deliver accurate, culturally sensitive results.
#MachineTranslation #AItranslation #TranslationErrors #LLMcontext #EUAIAct
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