1 code implementation • 2 May 2024 • Zhijing Jin, Yuen Chen, Fernando Gonzalez, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab
We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions, named entities, and in the final inference step where the LLM must connect its reasoning over the AMR to its prediction.
1 code implementation • 13 Sep 2023 • Gaotang Li, Jiarui Liu, Wei Hu
Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels.
1 code implementation • 21 Jun 2023 • Zheni Zeng, Bangchen Yin, Shipeng Wang, Jiarui Liu, Cheng Yang, Haishen Yao, Xingzhi Sun, Maosong Sun, Guotong Xie, Zhiyuan Liu
Natural language is expected to be a key medium for various human-machine interactions in the era of large language models.
1 code implementation • 9 Jun 2023 • Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, Bernhard Schölkopf
In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs).
1 code implementation • 24 May 2023 • Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schoelkopf, Zhijing Jin, Rada Mihalcea
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.
no code implementations • 28 Apr 2021 • Jiarui Liu
Additionally, we can use the PMR estimator to test for the true match value distribution in the data.