6 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
LightMTP: Lightweight Latent Multi-Token Prediction
Tamara Czinczoll, Julie Kallini, Gerard de Melo +1
Next-token prediction (NTP) is the standard pretraining objective for large language models, yet it provides an explicit training signal only for the immediate next token, which ca…
Toward a Unified Mathematics of Concepts
Chen Shani
Concepts are commonly defined as abstract, compact representations of knowledge and treated as basic units of intelligent behavior. Yet, cognition, psychology, and AI lack a shared…
Aligned Alone, Misaligned Together: Forecasting Adversarial Capture in LLM Agent Populations
Isotta Magistrali, Chen Shani
AI safety evaluations typically assess models one at a time. But language-model agents increasingly operate in populations, where they read and respond to one another's decisions.…
From Found to Designed: Concepts as a Design Axis for Large Language Models
Chen Shani
Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than as explicit, structured, com…
Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models
Christine Zhang, Dan Jurafsky, Chen Shani
The next-token prediction (NTP) objective trains language models to predict a single token at each step, even though many continuations can express the same meaning. For example, i…
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Chen Shani, Liron Soffer, Dan Jurafsky +2
Humans organize knowledge into compact conceptual categories that balance compression with semantic richness. Large Language Models (LLMs) exhibit impressive linguistic abilities,…