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Περιπτώσεις χρήσηςΑναζήτηση
Κατανοήστε τις μεθοδολογίες αξιολόγησης τεχνητής νοημοσύνης
Χρησιμοποιήστε την Εις βάθος έρευνα για να αποκωδικοποιήσετε τα σημεία αναφοράς LLM—πώς λειτουργούν, πού αποτυγχάνουν και ποιες μετρικές να εμπιστευτείτε για την αξιολόγηση μοντέλων
- Αναζήτηση
- Εις βάθος έρευνα
- Τεχνολογία
Επισκόπηση
Οι ερευνητές τεχνητής νοημοσύνης, οι μηχανικοί και οι ηγέτες προϊόντων μπορούν να χρησιμοποιήσουν το Perplexity για να κατανοήσουν το ραγδαία εξελissόμενο τοπίο της αξιολόγησης LLM. Η Εις βάθος έρευνα αντλεί στοιχεία από ακαδημαϊκές εργασίες, τεκμηρίωση σημείων αναφοράς και ανάλυση της βιομηχανίας για να εξηγήσει όχι μόνο πώς λειτουργούν τα σημεία αναφοράς, αλλά και πού υστερούν, βοηθώντας τις ομάδες να λαμβάνουν καλύτερες αποφάσεις σχετικά με το ποιες μετρήσεις πρέπει να εμπιστεύονται κατά την αξιολόγηση μοντέλων.
Συμβουλή
- Μοιραστείτε το συγκεκριμένο πλαίσιο παραγωγής σας (π.χ., αγωγός RAG, συγκυβερνήτης κsδικοποίησης, βοηθός υποστήριξης) ώστε το Perplexity να μπορεί να αντιστοιχίσει αυτά τα σημεία αναφοράς με τη σουίτα αξιολόγησης που θα θέλατε πραγματικά να δημιουργήσετε
- Ενημερωθείτε σχετικά με τις νεότερες προσεγγίσεις αξιολόγησης που αναπτύσσονται για την αντιμετώπιση αυτών των δομικών περιορισμών για ένα πλαίσιο με προοπτική στο μέλλον