AI Dictionary › Prompting
Analogical prompting is a technique in which the model is asked to recall or autonomously generate similar previously solved problems before tackling the specific problem posed by the user, so as to use those analogies as a guide for reasoning. The model identifies a recurring structure and applies it to the new case.
Analogical prompting is a technique in which the model is asked to recall or autonomously generate similar previously solved problems before tackling the specific problem posed by the user, so as to use those analogies as a guide for reasoning. The model identifies a recurring structure and applies it to the new case.
Unlike few-shot, where examples are supplied by the user in the prompt, here the model itself generates relevant analogous examples based on its own prior knowledge. The prompt typically asks it to identify one or more similar problems with their solutions, and then transfer the solving pattern to the original problem.
This technique proves effective in mathematical, logical or programming reasoning tasks, where recurring patterns exist that the model can recognize: faced with a new geometry exercise, the model can recall an analogous problem seen during training and adapt its procedure, or faced with a code bug it can recall similar error patterns encountered before.
The name derives from analogical reasoning, a cognitive mechanism long studied in psychology and artificial intelligence, adopted in prompting as an explicit technique once it was observed that inviting the model to generate similar examples on its own improves reasoning quality compared to asking directly for the solution.
L'analogical prompting e' una tecnica in cui si chiede al modello di richiamare o generare autonomamente problemi simili gia' risolti, prima di affrontare il problema specifico posto dall'utente, cosi' da usare quelle analogie come guida per il ragionamento. Il modello identifica una struttura ricorrente e la applica al caso nuovo.
A differenza del few-shot, dove gli esempi sono forniti dall'utente nel prompt, qui e' il modello stesso a generare esempi analoghi pertinenti al problema, basandosi sulla propria conoscenza pregressa. Il prompt chiede tipicamente di individuare uno o piu' problemi simili con relativa soluzione, per poi trasferire lo schema risolutivo al problema originale.
Questa tecnica si rivela efficace in compiti di ragionamento matematico, logico o di programmazione, dove esistono pattern ricorrenti che il modello puo' riconoscere: davanti a un nuovo esercizio di geometria, il modello puo' richiamare un problema analogo gia' visto durante l'addestramento e adattarne il procedimento, oppure davanti a un bug di codice puo' ricordare pattern di errore simili incontrati in passato.
Il nome deriva dal ragionamento per analogia, un meccanismo cognitivo studiato a lungo in psicologia e intelligenza artificiale, adottato nel prompting come tecnica esplicita quando si e' osservato che invitare il modello a generare da solo esempi simili migliora la qualita' del ragionamento rispetto al chiedere direttamente la soluzione.
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