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Found 9 papers, 7 papers with code
"On the goals of linguistic theory": Revisiting Chomskyan theories in the era of AI
We will argue that such models, in particular neural grammar induction models, do have a role to play, but that this role is largely modulated by the stance one takes regarding each of these three guiding principles.
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment
In this work, we systematically evaluate the efficacy of value networks and reveal their significant shortcomings in reasoning-heavy LLM tasks, showing that they barely outperform a random baseline when comparing alternative steps.
Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
To demonstrate the value of our dataset, we evaluate an AURORA-finetuned model on a new expert-curated benchmark (AURORA-Bench) covering 8 diverse editing tasks.
Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models
Here, we argue that they are instead both contingent on a more general learning strategy for language acquisition: joint learning.
Learning the meanings of function words from grounded language using a visual question answering model
Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning, as well as early evidence that they are sensitive to alternative expressions when interpreting language.
The Emergence of the Shape Bias Results from Communicative Efficiency
By the age of two, children tend to assume that new word categories are based on objects' shape, rather than their color or texture; this assumption is called the shape bias.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
A generalized parsing framework for Abstract Grammars
This technical report presents a general framework for parsing a variety of grammar formalisms.
Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical Report
The following technical report presents a formal approach to probabilistic minimalist grammar parameter estimation.
