5 papers
High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination
Sahaj Singh Maini, Robert L. Goldstone, Zoran Tiganj
Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question whether they can demonstrate comparabl…
Temporal Dependencies in In-Context Learning: The Role of Induction Heads
Anooshka Bajaj, Deven Mahesh Mistry, Sahaj Singh Maini +3
Large language models (LLMs) exhibit strong in-context learning capabilities, but how they track and retrieve information from context remains underexplored. Drawing on the free re…
Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models
Anooshka Bajaj, Deven Mahesh Mistry, Sahaj Singh Maini +2
In-context learning is governed by both temporal and semantic relationships, shaping how Large Language Models (LLMs) retrieve contextual information. Analogous to human episodic m…
Vision-language models learn the geometry of human perceptual space
Craig Sanders, Billy Dickson, Sahaj Singh Maini +2
In cognitive science and AI, a longstanding question is whether machines learn representations that align with those of the human mind. While current models show promise, it remain…
Emergence of Episodic Memory in Transformers: Characterizing Changes in Temporal Structure of Attention Scores During Training
Deven Mahesh Mistry, Anooshka Bajaj, Yash Aggarwal +2
We investigate in-context temporal biases in attention heads and transformer outputs. Using cognitive science methodologies, we analyze attention scores and outputs of the GPT-2 mo…