Stanford study challenges assumptions about language models: Larger context doesn’t mean better understanding

Recommendable! 

I have not read the underlying Stanford U study, but from my own understanding of computational linguistics and machine learning, the recent efforts to boost the context window size may indeed not be so advantageous. Or by itself it is not enough.

"A study released this month by researchers from Stanford University, UC Berkeley and Samaya AI has found that large language models (LLMs) often fail to access and use relevant information given to them in longer context windows. ...
But the study shows that some assumptions around the context window are flawed when it comes to the LLM’s ability to search and analyze it accurately. 
The study found that LLMs performed best “when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts. Furthermore, performance substantially decreases as the input context grows longer, even for explicitly long-context models.”"

Stanford study challenges assumptions about language models: Larger context doesn’t mean better understanding  | VentureBeat