The Irrational LLM: Implementing Cognitive Agents with Weighted Retrieval-Augmented Generation

This paper advances research on social networks, extended reality, and the metaverse by bringing together innovations from two different communities – AI and cognitive science – to develop LLM-based agents with not only fluent responses but also realistic opinion dynamics that capture a variety of human biases, imperfections, and general departures from rationality. This avenue of investigation can empower applications from social simulation of human opinions in geopolitical hotspots to realistic non-player character interactions in metaverse games. Recent advances in AI have made remarkable progress toward general intelligence with the introduction of large language models (LLMs). They also enabled grounding LLM responses in specialized information stored externally using retrieval-augmented generation (RAG). In a separate line of research, studies on human cognition have produced cognitive architectures that emulate human departures from rationality, such as biases and imperfections, which are crucial to understanding a wide range of social phenomena and human preferences. A critical mechanism in cognitive architectures is the modulation of retrieval weights from (human) memory; we are biased in what we remember. Combining RAG with cognitive model-inspired computation of information retrieval weights, we develop the Irrational LLM – one that weighs information retrieval in RAG systems according to cognitive models, thereby accurately emulating human opinion formation. We implement the novel human cognition-inspired RAG framework (CogRAG) and use it to emulate option developments on different sides of a conflict regarding debated issues. Responses generated by CogRAG (on posts withheld from training data) show close correspondence with real responses posted on social media, suggesting the viability of this approach in approximating biased human opinions. We hope this study paves the way to new directions in AI, social networks, metaverse computing, and human-in-the-loop modeling that better represent diverse human opinions in geopolitical, entertainment, and socio-technical contexts.