Esplorare le capacità dei grandi modelli linguistici nel risolvere analogie proporzionali tramite stimoli potenziati dalla conoscenza.
Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
December 1, 2024
Autori: Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, Amit Sheth
cs.AI
Abstract
Fare analogie è fondamentale per la cognizione. Le analogie proporzionali, che consistono in quattro termini, sono spesso utilizzate per valutare le capacità linguistiche e cognitive. Ad esempio, completare analogie come "L'ossigeno è al gas come <vuoto> è al <vuoto>" richiede l'identificazione del rapporto semantico (ad esempio, "tipo di") tra la prima coppia di termini ("Ossigeno" e "Gas") e trovare una seconda coppia che condivida lo stesso rapporto (ad esempio, "Alluminio" e "Metallo"). In questo lavoro, presentiamo un dataset di domande a scelta multipla da 15K (MCQA) per il completamento di analogie proporzionali e valutiamo le prestazioni dei contemporanei Grandi Modelli Linguistici (LLM) in vari contesti di prompt potenziati dalla conoscenza. In particolare, arricchiamo i prompt con tre tipi di conoscenza: esemplare, strutturata e mirata. I nostri risultati mostrano che nonostante l'ampio training data, risolvere analogie proporzionali rimane una sfida per i LLM attuali, con il miglior modello che raggiunge un'accuratezza del 55%. In particolare, scopriamo che fornire conoscenze mirate può aiutare meglio i modelli nel completare analogie proporzionali rispetto a fornire esempi o collezioni di conoscenze strutturate.
English
Making analogies is fundamental to cognition. Proportional analogies, which
consist of four terms, are often used to assess linguistic and cognitive
abilities. For instance, completing analogies like "Oxygen is to Gas as <blank>
is to <blank>" requires identifying the semantic relationship (e.g., "type of")
between the first pair of terms ("Oxygen" and "Gas") and finding a second pair
that shares the same relationship (e.g., "Aluminum" and "Metal"). In this work,
we introduce a 15K Multiple-Choice Question Answering (MCQA) dataset for
proportional analogy completion and evaluate the performance of contemporary
Large Language Models (LLMs) in various knowledge-enhanced prompt settings.
Specifically, we augment prompts with three types of knowledge: exemplar,
structured, and targeted. Our results show that despite extensive training
data, solving proportional analogies remains challenging for current LLMs, with
the best model achieving an accuracy of 55%. Notably, we find that providing
targeted knowledge can better assist models in completing proportional
analogies compared to providing exemplars or collections of structured
knowledge.Summary
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