Cover Image

Do Large Language Models Reason Causally Like Us? Even Better?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dettki, Hanna M., Lake, Brenden M., Wu, Charley M., Rehder, Bob
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Do LLMs Share Human-Like Biases? Causal Reasoning Under Prior Knowledge, Irrelevant Context, and Varying Compute Budgets
by: Dettki, Hanna M., et al.
Published: (2026)

Causal Strengths and Leaky Beliefs: Interpreting LLM Reasoning via Noisy-OR Causal Bayes Nets
by: Dettki, Hanna
Published: (2025)

Overcoming classic challenges for artificial neural networks by providing incentives and practice
by: Irie, Kazuki, et al.
Published: (2024)

Partially Observed Structural Causal Models
by: Orujlu, Turan, et al.
Published: (2026)

Compositional learning of functions in humans and machines
by: Zhou, Yanli, et al.
Published: (2024)

An explainable transformer circuit for compositional generalization
by: Tang, Cheng, et al.
Published: (2025)

Rapid Word Learning Through Meta In-Context Learning
by: Wang, Wentao, et al.
Published: (2025)

CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
by: Komanduri, Aneesh, et al.
Published: (2025)

CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
by: Zan, Lei, et al.
Published: (2025)

Discovering and Reasoning of Causality in the Hidden World with Large Language Models
by: Liu, Chenxi, et al.
Published: (2024)

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
by: Manvi, Rohin, et al.
Published: (2024)

Do Large Language Models Show Biases in Causal Learning?
by: Carro, Maria Victoria, et al.
Published: (2024)

Compositional Causal Reasoning Evaluation in Language Models
by: Maasch, Jacqueline R. M. A., et al.
Published: (2025)

Causality for Large Language Models
by: Wu, Anpeng, et al.
Published: (2024)

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
by: Chi, Haoang, et al.
Published: (2025)

Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)

Position: What Can Large Language Models Tell Us about Time Series Analysis
by: Jin, Ming, et al.
Published: (2024)

Better Decisions through the Right Causal World Model
by: Dillies, Elisabeth, et al.
Published: (2025)

Analogy making as amortised model construction
by: Nagy, David G., et al.
Published: (2025)

Revealing Multimodal Causality with Large Language Models
by: Li, Jin, et al.
Published: (2025)

A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Services
by: Pan, Guanzhong, et al.
Published: (2025)

Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models
by: Sathyanarayanan, Anish, et al.
Published: (2026)

Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
by: Zakazov, Ivan, et al.
Published: (2024)

Agent-centric learning: from external reward maximization to internal knowledge curation
by: Zhou, Hanqi, et al.
Published: (2025)

CoLLEGe: Concept Embedding Generation for Large Language Models
by: Teehan, Ryan, et al.
Published: (2024)

Do Transformer World Models Give Better Policy Gradients?
by: Ma, Michel, et al.
Published: (2024)

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
by: Cai, Hengrui, et al.
Published: (2023)

QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
by: Tseng, Albert, et al.
Published: (2024)

CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection
by: Wang, Ruiqi, et al.
Published: (2026)

Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation
by: Jeong, Jiwon, et al.
Published: (2025)

CLadder: Assessing Causal Reasoning in Language Models
by: Jin, Zhijing, et al.
Published: (2023)

A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
by: Xie, Chengxing, et al.
Published: (2024)

Do Large Language Models (LLMs) Understand Chronology?
by: Wongchamcharoen, Pattaraphon Kenny, et al.
Published: (2025)

ReasoningWeekly: A General Knowledge and Verbal Reasoning Challenge for Large Language Models
by: Wu, Zixuan, et al.
Published: (2025)

How Likely Do LLMs with CoT Mimic Human Reasoning?
by: Bao, Guangsheng, et al.
Published: (2024)

Large Language Models are Effective Priors for Causal Graph Discovery
by: Darvariu, Victor-Alexandru, et al.
Published: (2024)

Optimizing Prompts for Large Language Models: A Causal Approach
by: Chen, Wei, et al.
Published: (2026)

Are Large-Language Models Graph Algorithmic Reasoners?
by: Taylor, Alexander K, et al.
Published: (2024)

What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure
by: Budding, Céline
Published: (2025)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
by: Takayama, Masayuki, et al.
Published: (2024)