Published in , 2022
This paper is about the a dataset of Logical Fallacy Detection and its baseline model
Recommended citation: Jin, Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical Fallacy Detection. https://arxiv.org/abs/2202.13758
Published in , 2023
This paper is about a prompting method embedded causal direction and analyze the performance gap of LLMs
Recommended citation: Lyu, Z., Jin, Z., Mattern, J., Mihalcea, R., Sachan, M., & Schoelkopf, B. (2023). Psychologically-Inspired Causal Prompts. arXiv preprint arXiv:2305.01764. https://arxiv.org/pdf/2305.01764
Published in , 2023
Our paper conducts a post-hoc analysis to check whether large language models can be used to distinguish cause from effect.
Recommended citation: Jin, Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical Fallacy Detection. https://openreview.net/forum?id=ucHh-ytUkOH
Published in , 2023
This research introduces the first benchmark dataset, Corr2Cause, to test large language models (LLMs) pure causal inference skills.
Recommended citation: Jin Z, Liu J, Lyu Z, et al. Can Large Language Models Infer Causation from Correlation? arXiv preprint arXiv:2306.05836, 2023. https://arxiv.org/abs/2306.05836