Failure Modes of LLMs for Causal Reasoning on Narratives (2410.23884v5)

Abstract: The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning. In this work, we investigate the interaction between these two capabilities through the representative task of causal reasoning over narratives. Through controlled synthetic, semi-synthetic, and real-world experiments, we find that state-of-the-art LLMs often rely on superficial heuristics -- for example, inferring causality from event order or recalling memorized world knowledge without attending to context. Furthermore, we show that simple reformulations of the task can elicit more robust reasoning behavior. Our evaluation spans a range of causal structures, from linear chains to complex graphs involving colliders and forks. These findings uncover systematic patterns in how LLMs perform causal reasoning and lay the groundwork for developing methods that better align LLM behavior with principled causal inference.

• The paper identifies key failure modes in LLMs’ causal reasoning, particularly when narratives deviate from canonical order.
• It demonstrates that LLM reliance on pretrained parametric biases can override logical inference from narrative contexts.
• The study reveals that longer narratives exacerbate reasoning challenges and proposes causal graph extraction as a method for improvement.

An Analysis of Failure Modes in Causal Reasoning with LLMs

In the paper "Failure Modes of LLMs for Causal Reasoning on Narratives," the authors embark on a comprehensive examination of the capabilities and limitations of state-of-the-art LLMs as they engage in causal reasoning from narrative texts. Their research zeroes in on understanding how these models perform in determining causality within narratives, specifically when discerning the causal relationships between events described in narrative form. The investigation unveils notable inadequacies and introduces methods that might offer improvements.

The essence of causal reasoning lies in ascertaining the relationships that denote cause and effect, moving beyond the mere observation of coinciding events. It's a fundamental aspect of decision-making and intelligent behavior. As LLMs continue to evolve, there arises a need to understand their capacity for such reasoning, particularly as prior works demonstrate these models' propensity for mere memorization of causal assertions instead of genuine inference.

Numerical Findings and Observations

The authors highlight key failure modes where LLMs consistently falter. One major failure mode is the reliance on the narrative presentation's topological order, where LLMs struggle with reversing or unconventional sequence presentations. Their experiments reveal that when narratives follow the causal order, LLMs perform significantly better than when presented in reverse—or non-canonical—orders. In quantitative terms, narrative structuring that follows causal topologies consistently garnered higher accuracy, whereas deviations resulted in noticeable performance declines.

Another critical area of concern is the LLMs' dependency on parametric knowledge, which supersedes logical inference from narrative contexts, leading to errors when the narrative contradicts this ingrained information. This failure mode underscores the models' reliance on memorized knowledge instead of engaging with the narrative text per its framing. The paper cites several examples where causative structures presented in actual narratives were overridden by the model's inherent biases, with LLMs showing a reduced capacity to adapt the contextual logic contained within the narrative against their general, pre-trained knowledge.

Furthermore, the paper elucidates another detriment: LLMs' diminishing causal reasoning performance as narrative lengths increase. Longer narratives exacerbate reasoning challenges, underlining the importance of context completeness and retention among these models.

Theoretical and Practical Implications

These findings carry far-reaching implications for the practical use and theoretical understanding of LLMs in causal reasoning tasks. Practically, these failure modes reveal core limitations that can undermine the reliability of LLMs when deployed in domains requiring accurate causal reasoning, such as in automated deduction, decision-making systems, and AI-driven analyses that interpret narratives or reports.

From a theoretical lens, the paper extends discussions around bridging the gap between memorized knowledge and dynamic inference capabilities inherent in machine learning models. The exploration suggests pathways to enhance model fidelity in complex reasoning tasks, such as recalibrating attention mechanisms to improve narrative comprehension fidelity and reduce reliance on parametric fallbacks.

The introduction of a causal graph extraction technique presents a novel approach to mitigate some identified deficiencies. By explicitly generating a causal graph from narratives, this method holds promise to bolster reasoning performance by correcting the aforementioned failure modes. It underscores a potential line of future research that could blend graph-based reasoning frameworks with LLMs for improved causal inference.

Future Directions

This paper sets the stage for potential research directions involving refining methodologies to enhance LLMs' causal reasoning, especially amid unconventionally structured narratives, and improving their ability to override parametric shortcuts with logic elucidated from current contexts. Additionally, future exploration could delve into evaluating counterfactual reasoning and assessing LLMs' performance in more intricate causal frameworks beyond simple chain structures. Moreover, developing algorithms for finetuning or retraining could further address the limitations highlighted.

In conclusion, "Failure Modes of LLMs for Causal Reasoning on Narratives" presents an in-depth and methodologically varied approach to diagnosing and understanding the challenges LLMs face in causal reasoning. It offers insightful data-driven revelations that lay down a foundation for future innovations aimed at bolstering the cognitive faculties of these ever-evolving models.