Mission: Impossible language models – Paper Explained [ACL 2024 recording]

Julie Kallini presents insights from the paper "Mission Impossible Language Models," which challenges Noam Chomsky's assertion that language models cannot distinguish between possible and impossible languages. By defining a continuum of impossibility and testing various synthetic languages on a GPT-2 model, Kallini's findings indicate that the model struggled more with languages deemed more "impossible." The study highlights the relationship between language complexity and model perplexity, providing clarity on the capabilities of language models in learning grammar complexities.

Key AI Highlights in this Video

00:11 - 00:16

Julie Kallini introduces the mission to challenge Chomsky's views on LLMs.

01:16 - 01:33

Chomsky's claims on LLMs' inability to distinguish possible from impossible languages are presented.

02:50 - 03:02

Findings reveal that GPT-2 struggles more with increasingly impossible languages.

08:05 - 08:15

Higher entropy languages pose greater challenges for both humans and AI models.

AI Expert Commentary about this Video

AI Linguistics Expert

This research underscores the limitations of current language models against theoretical linguistic hypotheses. The claim that LLMs can learn impossible languages fails to account for the structure and predictability essential for language learning, revealing fundamental constraints in model training that merit further exploration.

AI Model Architect Expert

The findings provide critical insights into model architecture and training efficiencies. As models grapple with complexities and entropy in languages, optimizing training datasets could enhance their performance. Future developments should consider a spectrum of syntactically intricate structures to refine LLM capabilities further.