Failure Modes of LLMs for Causal Reasoning on Narratives

Bryan Wilder; Gaurav R. Ghosal; Khurram Yamin; Shantanu Gupta; Zachary C. Lipton

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Failure Modes of LLMs for Causal Reasoning on Narratives

Khurram Yamin, Shantanu Gupta, Gaurav R. Ghosal, Zachary C. Lipton, Bryan Wilder

Machine Learning · Submitted 31 Oct 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Through controlled synthetic, semi-synthetic, and real-world experiments, we find that state-of-the-art large language models (LLMs) often rely on superficial heuristics -- for example, inferring causality from event order or recalling memorized world knowledge without attending to context.

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CitationBryan Wilder, Gaurav R. Ghosal, Khurram Yamin, Shantanu Gupta, Zachary C. Lipton. "Failure Modes of LLMs for Causal Reasoning on Narratives." arXiv:2410.23884 (2024). https://pith.science/paper/TLIDVSUQ

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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 large language models (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.

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s0	The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios.	background 1.00	0.04	0.02	asserted 1.00
s1	However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning.	background 0.97	0.06	0.03	asserted 1.00
s2	In this work, we investigate the interaction between these two capabilities through the representative task of causal reasoning over narratives.	contribution 0.98	0.64	0.03	asserted 0.86
s3	Through controlled synthetic, semi-synthetic, and real-world experiments, we find that state-of-the-art large language models (LLMs) often rely on superficial heuristics -- for example, inferring causality from event order or recalling memorized world knowledge without attending to context.	result 1.00	0.97	0.63	asserted 1.00
s4	Furthermore, we show that simple reformulations of the task can elicit more robust reasoning behavior.	result 0.96	0.93	0.15	asserted 0.99
s5	Our evaluation spans a range of causal structures, from linear chains to complex graphs involving colliders and forks.	method 0.83	0.89	0.04	asserted 0.98
s6	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.	interpretation 0.96	0.80	0.62	asserted 0.96

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Original 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 large language models (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.

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8 arXiv papers cite this in 11 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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In transactional scenarios, these validation gaps manifest as factual inconsistencies, invalid operations, and unreliable plan feasibility assessments [ref] .

SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning · LLM Limitations Necessitating SagaLLM's Key Requirements · 1.00
Reasoning-label bias is affected by several factors, including the absence and error of inference logic [ref] , the potential association between the head entity and the entity to be predicted [ref] , and the order of causal relations in the narrative [ref] .

Shortcut Learning in In-Context Learning: A Survey · Instinctive Shortcuts · 0.98

Cites it as background 1 paper

* Reasoning is a process of integrating multiple types of knowledge to draw new conclusions about the world [ref] , which challenges LLMs' ability to perform mathematical reasoning [ref] , causal reasoning [ref] , multi-hop reasoning, and other complex cognitive processes [ref] .

Shortcut Learning in In-Context Learning: A Survey · Tasks · 0.92

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We also explore many mediums of prompting including Chain of Thought (COT) [ref] and In-Context Learning [ref] (see Appendix [ref] for the prompts).

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models · Data Generation Process · 0.99
We also explore many mediums of prompting including Chain of Thought (COT) [ref] and In-Context Learning [ref] (see Appendix [ref] for the prompts).

A Survey on In-context Learning · Data Generation Process · 0.99

Cites as support 2 papers

While many works have shown that LLMs are capable of memorizing and recalling causal knowledge, they can fail at reliably leveraging that knowledge for reasoning [ref] .

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal · Introduction · 0.97
This finding is supported by the well known phenomenon that LLMs often fail to reason about longer form contexts especially as they “get lost in the middle" [ref] .

Lost in the Middle: How Language Models Use Long Contexts · Failure of Long-Term Causal Reasoning · 1.00

Cites as background 9 papers

Extensive prior work has also studied methods for improving the reasoning capabilities of large language models such as by eliciting step-by-step explanations [ref] , finetuning on reasoning traces [ref] , and training models to find and correct errors [ref] .

STaR: Bootstrapping Reasoning With Reasoning · Related Works · 0.96
Extensive prior work has also studied methods for improving the reasoning capabilities of large language models such as by eliciting step-by-step explanations [ref] , finetuning on reasoning traces [ref] , and training models to find and correct errors [ref] .

Training Language Models to Self-Correct via Reinforcement Learning · Related Works · 0.96
Extensive prior work has also studied methods for improving the reasoning capabilities of large language models such as by eliciting step-by-step explanations [ref] , finetuning on reasoning traces [ref] , and training models to find and correct errors [ref] .

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models · Related Works · 0.96

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