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20162024
most citedLanguage Models Trained on Media Diets Can Predict Public Opinion

23 citations · 78 across the 16 of their papers we have counts for

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10 papers · 1 filter

cs.CL2024

Learning Phonotactics from Linguistic Informants

Canaan Breiss, Alexis Ross, Amani Maina-Kilaas +2

We propose an interactive approach to language learning that utilizes linguistic acceptability judgments from an informant (a competent language user) to learn a grammar. Given a g…

cs.CL2024

Toward In-Context Teaching: Adapting Examples to Students' Misconceptions

Alexis Ross, Jacob Andreas

When a teacher provides examples for a student to study, these examples must be informative, enabling a student to progress from their current state toward a target concept or skil…

cs.CL2024

Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling

Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas

Today's most accurate language models are trained on orders of magnitude more language data than human language learners receive - but with no supervision from other sensory modali…

cs.CL2024

Bayesian Preference Elicitation with Language Models

Kunal Handa, Yarin Gal, Ellie Pavlick +4

Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences. Recently, language models (LMs) have been used to gather in…

cs.CL2023

Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning

Athul Paul Jacob, Gabriele Farina, Jacob Andreas

We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model (whic…

cs.CL2023

Pushdown Layers: Encoding Recursive Structure in Transformer Language Models

Shikhar Murty, Pratyusha Sharma, Jacob Andreas +1

Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently…