Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?

Language model (LM) agents are increasingly used as autonomous decision-makers who need to actively gather information to guide their decisions. A crucial cognitive skill for such agents is the efficient exploration and understanding of the causal structure of the world -- key to robust, scientifically grounded reasoning. Yet, it remains unclear whether LMs possess this capability or exhibit systematic biases leading to erroneous conclusions. In this work, we examine LMs' ability to explore and infer causal relationships, using the well-established "Blicket Test" paradigm from developmental psychology. We find that LMs reliably infer the common, intuitive disjunctive causal relationships but systematically struggle with the unusual, yet equally (or sometimes even more) evidenced conjunctive ones. This "disjunctive bias" persists across model families, sizes, and prompting strategies, and performance further declines as task complexity increases. Interestingly, an analogous bias appears in human adults, suggesting that LMs may have inherited deep-seated reasoning heuristics from their training data. To this end, we quantify similarities between LMs and humans, finding that LMs exhibit adult-like inference profiles (but not children-like). Finally, we propose a test-time sampling method which explicitly samples and eliminates hypotheses about causal relationships from the LM. This scalable approach significantly reduces the disjunctive bias and moves LMs closer to the goal of scientific, causally rigorous reasoning.

Fine-tuned network relies on generic representation to solve unseen cognitive task

Fine-tuning pretrained language models has shown promising results on a wide range of tasks, but when encountering a novel task, do they rely more on generic pretrained representation, or develop brand new task-specific solutions? Here, we fine-tuned GPT-2 on a context-dependent decision-making task, novel to the model but adapted from neuroscience literature. We compared its performance and internal mechanisms to a version of GPT-2 trained from scratch on the same task. Our results show that fine-tuned models depend heavily on pretrained representations, particularly in later layers, while models trained from scratch develop different, more task-specific mechanisms. These findings highlight the advantages and limitations of pretraining for task generalization and underscore the need for further investigation into the mechanisms underpinning task-specific fine-tuning in LLMs.