no code implementations • 11 Mar 2024 • Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
no code implementations • 15 Feb 2024 • Ruichen Li, Chuwei Wang, Haotian Ye, Di He, LiWei Wang
Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems.
no code implementations • 4 Feb 2024 • Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, ZiHao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.
1 code implementation • 12 Jan 2024 • Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schütze
As a result, mPLMs present a script barrier: representations from different scripts are located in different subspaces, which is a strong indicator of why crosslingual transfer involving languages of different scripts shows sub-optimal performance.
no code implementations • 9 Jan 2024 • Haotian Ye, Yihong Liu, Chunlan Ma, Hinrich Schütze
In this paper, we introduce MoSECroT Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer), a novel and challenging task that is especially relevant to low-resource languages for which static word embeddings are available.
1 code implementation • 11 Nov 2023 • Sheng Liu, Haotian Ye, Lei Xing, James Zou
On a new query, instead of adding demonstrations to the prompt, we shift the latent states of the LLM using the ICV.
2 code implementations • 17 Jul 2023 • Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, LiWei Wang
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry.
no code implementations • NeurIPS 2023 • Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, LiWei Wang
By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length.
no code implementations • 22 May 2023 • Haotian Ye, Yihong Liu, Hinrich Schütze
An interesting line of research in natural language processing (NLP) aims to incorporate linguistic typology to bridge linguistic diversity and assist the research of low-resource languages.
2 code implementations • 22 May 2023 • Yihong Liu, Haotian Ye, Leonie Weissweiler, Renhao Pei, Hinrich Schütze
ColexNet's nodes are concepts and its edges are colexifications.
3 code implementations • 15 May 2023 • Yihong Liu, Haotian Ye, Leonie Weissweiler, Philipp Wicke, Renhao Pei, Robert Zangenfeind, Hinrich Schütze
The resulting measure for the conceptual similarity of two languages is complementary to standard genealogical, typological, and surface similarity measures.
no code implementations • 15 May 2023 • Chunlan Ma, Ayyoob ImaniGooghari, Haotian Ye, Ehsaneddin Asgari, Hinrich Schütze
While natural language processing tools have been developed extensively for some of the world's languages, a significant portion of the world's over 7000 languages are still neglected.
1 code implementation • 7 Dec 2022 • Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt
Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect.
1 code implementation • 20 Oct 2022 • Haotian Ye, James Zou, Linjun Zhang
This opens a promising strategy to first train a feature learner rather than a classifier, and then perform linear probing (last layer retraining) in the test environment.
no code implementations • 19 Oct 2022 • Haotian Ye, Xiaoyu Chen, LiWei Wang, Simon S. Du
Generalization in Reinforcement Learning (RL) aims to learn an agent during training that generalizes to the target environment.
no code implementations • NeurIPS 2021 • Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, LiWei Wang
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
no code implementations • 21 Jan 2021 • Haotian Ye, Chuanlong Xie, Yue Liu, Zhenguo Li
One of the definitions of OOD accuracy is worst-domain accuracy.
no code implementations • 13 Jun 2020 • Chuanlong Xie, Haotian Ye, Fei Chen, Yue Liu, Rui Sun, Zhenguo Li
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.