Causality for Natural Language Processing
On the map: LLM reasoning → Causal reasoning in LLMs
Abstract (arXiv)
Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. This thesis delves into various dimensions of causal reasoning and understanding in large language models (LLMs). It encompasses a series of studies that explore the causal inference skills of LLMs, the mechanisms behind their performance, and the implications of causal and anticausal learning for natural language processing (NLP) tasks. Additionally, it investigates the application of causal reasoning in text-based computational social science, specifically focusing on political decision-making and the evaluation of scientific impact through citations. Through novel datasets, benchmark tasks, and methodological frameworks, this work identifies key challenges and opportunities to improve the causal capabilities of LLMs, providing a comprehensive foundation for future research in this evolving field.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. First posted on arXiv 20 Apr 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Large Language Models and Causal Inference in Collaboration: A Survey
- 2023CLadder: Assessing Causal Reasoning in Language Models
- 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
- 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
- 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
- 2024Causal Inference with Large Language Model: A Survey
- 2024CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
- 2026CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching