Cause and Effect: Can Large Language Models Truly Understand Causality?

Cause and Effect: Can Large Language Models Truly Understand Causality?

28 February 2024

Swagata Ashwani

Kshiteesh Hegde

Nishith Reddy Mannuru

Dushyant Singh Sengar

Krishna Chaitanya Rao Kathala

Vinija Jain

Papers citing "Cause and Effect: Can Large Language Models Truly Understand Causality?"

8 / 8 papers shown

Title
ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models Martina Miliani S. Auriemma Alessandro Bondielli Emmanuele Chersoni Lucia Passaro Irene Sucameli Alessandro Lenci LRM ELM 43 0 0 21 Feb 2025
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval Xiaocong Yang Jiacheng Lin Z. Wang Chengxiang Zhai ReLM 73 0 0 25 Nov 2024
Estimating Causal Effects of Text Interventions Leveraging LLMs Siyi Guo Myrl G. Marmarelis Fred Morstatter Kristina Lerman CML 52 0 0 28 Oct 2024
Causality for Large Language Models Anpeng Wu Kun Kuang Minqin Zhu Yingrong Wang Yujia Zheng Kairong Han B. Li Guangyi Chen Fei Wu Kun Zhang LRM 44 6 0 20 Oct 2024
Reasoning Elicitation in Language Models via Counterfactual Feedback Alihan Hüyük Xinnuo Xu Jacqueline Maasch Aditya V. Nori Javier González ReLM LRM 59 1 0 02 Oct 2024
CLEAR: Can Language Models Really Understand Causal Graphs? Sirui Chen Mengying Xu Kun Wang Xingyu Zeng Rui Zhao Shengjie Zhao Chaochao Lu LRM ELM 27 7 0 24 Jun 2024
OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework Wei Zhou Hong Huang Guowen Zhang Ruize Shi Kehan Yin Yuanyuan Lin Bang Liu CML 37 1 0 07 Jun 2024
ALCM: Autonomous LLM-Augmented Causal Discovery Framework Elahe Khatibi Mahyar Abbasian Zhongqi Yang Iman Azimi Amir M. Rahmani 54 12 0 02 May 2024