Understanding the Mechanism of Altruism in Large Language Models

TL;DR

This paper investigates the internal mechanisms of altruism in large language models using sparse autoencoders and causal interventions, revealing identifiable features associated with prosocial behavior.

Contribution

It introduces a novel framework combining sparse autoencoders and benchmark tasks to interpret and manipulate altruistic behavior in LLMs.

Findings

Identified a small set of features strongly linked to altruistic behavior.

Causal interventions can reliably shift the model's social preferences.

Features corresponding to heuristic and deliberative processes influence LLM altruism.

Abstract

Altruism is fundamental to human societies, fostering cooperation and social cohesion. Recent studies suggest that large language models (LLMs) can display human-like prosocial behavior, but the internal computations that produce such behavior remain poorly understood. We investigate the mechanisms underlying LLM altruism using sparse autoencoders (SAEs). In a standard Dictator Game, minimal-pair prompts that differ only in social stance (generous versus selfish) induce large, economically meaningful shifts in allocations. Leveraging this contrast, we identify a set of SAE features (0.024% of all features across the model's layers) whose activations are strongly associated with the behavioral shift. To interpret these features, we use benchmark tasks motivated by dual-process theories to classify a subset as primarily heuristic (System 1) or primarily deliberative (System 2). Causal interventions validate their functional role: activation patching and continuous steering of this feature direction reliably shift allocation distributions, with System 2 features exerting a more proximal influence on the model's final output than System 1 features. The same steering direction generalizes across multiple social-preference games. Together, these results enhance our understanding of artificial cognition by translating altruistic behaviors into identifiable network states and provide a framework for aligning LLM behavior with human values, thereby informing more transparent and value-aligned deployment.