The field of large language models (LLMs) is moving towards improving their ability to perform causal reasoning, a crucial aspect of human-like intelligence. Recent research has highlighted the limitations of current LLMs in this regard, including their tendency to rely on parametric knowledge and struggle with counterfactual reasoning. However, innovative approaches such as incorporating general knowledge and goal-oriented prompts into LLMs' causal reasoning processes have shown promise in enhancing their causal reasoning capabilities. Noteworthy papers in this area include those that introduce new benchmarks and frameworks for evaluating and improving LLMs' causal reasoning abilities, such as BLANCE and FANTOM, which have demonstrated state-of-the-art performance in causal discovery and structure learning. Additionally, papers like Unveiling Causal Reasoning in Large Language Models and CLEAR-3K have provided valuable insights into the current limitations of LLMs and the need for more advanced causal reasoning capabilities.
Causal Reasoning in Large Language Models
Sources
LLMs Struggle to Perform Counterfactual Reasoning with Parametric Knowledge
Under the Shadow of Babel: How Language Shapes Reasoning in LLMs
Think Global, Act Local: Bayesian Causal Discovery with Language Models in Sequential Data
Flow-Based Non-stationary Temporal Regime Causal Structure Learning
CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?