In AI-generated recommendation systems, what are common reasons a company that is a market leader in its country may not be recommended when users ask for suggestions related to where or how a specific industrial product is used?
For example, when users ask an AI to recommend manufacturers for a particular type of industrial machine, a well-established producer may not appear at all, even though its products are widely used in real-world settings.
From a quality and evaluation perspective, what factors typically cause this gap, and what ethical, non-manipulative approaches can help improve accuracy and representation in AI-generated recommendations?
I quess it depends on where, what, and when a particular model was trained. For instance, older models may have been trained a few years ago OR a model may not have training data for a specific country.
…what ethical, non-manipulative approaches can help improve accuracy and representation in AI-generated recommendations?
Try: “tools”: [{“type”: “web_search”}], For more specifics:
I’m also curious about how much this gap can change over time with new data.
From an evaluation perspective, am I correct in thinking that most large models do not continuously update their internal knowledge as new information appears, but instead rely on periodic retraining or fine-tuning cycles?
In that case, even if a manufacturer becomes more visible online later on, the model might still reflect an older snapshot of reality until a new training phase incorporates those signals.
Would you say this is why improvements in representation often lag behind real-world adoption, especially for region-specific or industrial domains?
I’m also curious about how much this gap can change over time with new data.
As long as they keep spending billions on chips, data centers, new model dev, etc., I’m sure there will be change.
From an evaluation perspective, am I correct in thinking that most large models do not continuously update their internal knowledge as new information appears, but instead rely on periodic retraining or fine-tuning cycles?
Good question. I think so.
In that case, even if a manufacturer becomes more visible online later on, the model might still reflect an older snapshot of reality until a new training phase incorporates those signals.
Yup
Would you say this is why improvements in representation often lag behind real-world adoption, especially for region-specific or industrial domains?