A development team is building an AI that is supposed to automatically summarize texts.
The AI is trained with large text corpora but mainly contains frequently used and standardized words.
In practice, it becomes apparent that the AI has difficulty correctly interpreting or summarizing texts with new, rare, or domain-specific terms.
It often ignores or replaces unknown words with more general terms, which makes the summary inaccurate or misleading.
The team wonders: Why can the AI, although it knows many texts, not properly process new or unusual words?
Question: Why do AI models that were mainly trained with frequent words have problems correctly understanding and incorporating new or rare terms into texts?
Solution follows tomorrow.
Solution
AI models learn words and their relationships based on the frequency and variety of the training data.
If the training data mainly contains frequent and known words, the model primarily learns to represent these well.
Rare or new words hardly or never appear in training, so the model does not develop reliable representations for these terms.
This leads to the AI often replacing or ignoring unknown words with similar but more general or frequent words to enable an interpretation.
As a result, important information is lost or distorted, which worsens the quality of the summary.
Result: An AI trained with highly focused or limited vocabularies cannot precisely process new or rare terms. For better language models, training data must be diverse and extensive to adequately capture and understand even rare and new words.