Buonasera, 380°

Il 15/10/2023 20:21, 380° ha scritto:
>
> Allora ripeto la mia domanda: ci sono nuovi studi che dimostrino che le
> limitazioni evidenziate nei test sulla competenza logico/linguistica di
> BERT siano stati risolti da altri LLM?
>

Tra le pubblicazioni recenti, sul tema, segnalo

Vittoria Dentella, Elliot Murphy, Gary Marcus, Evelina Leivada, Testing AI 
performance on less frequent aspects of language reveals insensitivity to 
underlying meaning, 2023
https://arxiv.org/abs/2302.12313

Abstract
Advances in computational methods and big data availability have recently 
translated into breakthroughs in AI applications. With successes in bottom-up 
challenges partially overshadowing shortcomings, the 'human-like' performance 
of Large Language Models has raised the question of how linguistic performance 
is achieved by algorithms. Given systematic shortcomings in generalization 
across many AI systems, in this work we ask whether linguistic performance is 
indeed guided by language knowledge in Large Language Models. To this end, we 
prompt GPT-3 with a grammaticality judgement task and comprehension questions 
on less frequent constructions that are thus unlikely to form part of Large 
Language Models' training data. These included grammatical 'illusions', 
semantic anomalies, complex nested hierarchies and self-embeddings. GPT-3 
failed for every prompt but one, often offering answers that show a critical 
lack of understanding even of high-frequency words used in these less frequent 
grammatical constructions. The present work sheds light on the boundaries of 
the alleged AI human-like linguistic competence and argues that, far from 
human-like, the next-word prediction abilities of LLMs may face issues of 
robustness, when pushed beyond training data. 

Ho intravisto anche (ma non l'ho letto)

Konstantine Arkoudas, GPT-4 Can’t Reason, 2023, https://arxiv.org/abs/2308.03762

Buona serata,
Daniela
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