5 papers
Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search
Haijiang Yan, Jian-Qiao Zhu, Liqiang Huang +1
Humans often find good solutions to combinatorial optimization problems that are computationally hard even for advanced computer algorithms. In the Euclidean traveling salesman pro…
Large Language Models Can Take False First Steps at Inference-time Planning
Haijiang Yan, Jian-Qiao Zhu, Adam Sanborn
Large language models (LLMs) have been shown to acquire sequence-level planning abilities during training, yet their planning behavior exhibited at inference time often appears sho…
Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
Changing the behavior of large language models (LLMs) can be as straightforward as editing the Transformer's residual streams using appropriately constructed "steering vectors." Th…
Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
Rational decision-making under uncertainty requires coherent degrees of belief in events. However, event probabilities generated by Large Language Models (LLMs) have been shown to…
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
The observed similarities in the behavior of humans and Large Language Models (LLMs) have prompted researchers to consider the potential of using LLMs as models of human cognition.…