mlsanityData to 7 Oct 2026

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

arXiv 2409.00103 ↗First posted 27 Aug 2024cs.CL cs.AIcited by 1

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating intermediates with nuanced differences in causal reasoning. We propose a suite of novel metrics -- intensity ranking concordance, cross-group position agreement, and intra-group clustering -- to evaluate LLMs on this front. Through extensive empirical studies on 21 high-profile LLMs, including GPT-4, Claude3, and LLaMA3-70B, we have favoring evidence that current models struggle to maintain epistemic consistency in identifying the polarity and intensity of intermediates in causal reasoning. Additionally, we explore the potential of using internal token probabilities as an auxiliary tool to maintain causal epistemic consistency. In summary, our study bridges a critical gap in AI research by investigating the self-consistency over fine-grained intermediates involved in causal reasoning.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 5 of its 18 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

5 of 18 references.

References · 5

  1. 2020Scaling Laws for Neural Language Modelscited by 6,411
  2. 2023Can Large Language Models Infer Causation from Correlation?cited by 142
  3. 2024Look Within, Why LLMs Hallucinate: A Causal Perspectivecited by 11
  4. 2024A Logical Fallacy-Informed Framework for Argument Generationcited by 7
  5. 2024The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoningcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model Statescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
  2. 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
  3. 2026Does Inference Scaling Improve Reasoning Faithfulness? A Multi-Model Analysis of Self-Consistency Tradeoffs
  4. 2023CLadder: Assessing Causal Reasoning in Language Models
  5. 2026Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
  6. 2023Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
  7. 2024CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
  8. 2026Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty