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2020 – today
- 2024
[c30]Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, Tianxing He:
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text. ACL (Findings) 2024: 1706-1715- [c29]Wenxuan Ding, Shangbin Feng, Yuhan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models. ACL (Findings) 2024: 2609-2636 - [c28]Yichen Wang, Shangbin Feng, Abe Bohan Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, Tianxing He:
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks. ACL (1) 2024: 2894-2925 - [c27]Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models. ICLR 2024 - [c26]Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Niloofar Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov:
LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud. NAACL-HLT (Findings) 2024: 2674-2690 - [c25]Abe Bohan Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov:
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation. NAACL-HLT 2024: 4067-4082 - [c24]Yuyang Bai
, Shangbin Feng, Vidhisha Balachandran, Zhaoxuan Tan, Shiqi Lou, Tianxing He, Yulia Tsvetkov:
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models. WWW 2024: 2226-2237 - [i29]Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, Tianxing He:
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text. CoRR abs/2402.11399 (2024) - [i28]Yichen Wang, Shangbin Feng, Abe Bohan Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, Tianxing He:
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks. CoRR abs/2402.11638 (2024) - [i27]Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov:
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically. CoRR abs/2404.16367 (2024) - [i26]Jingwei Li, Jing Dong, Tianxing He, Jingzhao Zhang:
Towards Black-Box Membership Inference Attack for Diffusion Models. CoRR abs/2405.20771 (2024) - [i25]Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xiaochuang Han, Tianxing He, Yulia Tsvetkov:
Can LLM Graph Reasoning Generalize beyond Pattern Memorization? CoRR abs/2406.15992 (2024) - 2023
- [c23]Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James R. Glass, Yulia Tsvetkov:
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation. ACL (1) 2023: 12067-12097 - [c22]Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, Tianxing He:
On the Zero-Shot Generalization of Machine-Generated Text Detectors. EMNLP (Findings) 2023: 4799-4808 - [c21]Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül:
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics. NeurIPS 2023 - [c20]Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov:
Can Language Models Solve Graph Problems in Natural Language? NeurIPS 2023 - [c19]Jingyu Zhang, James R. Glass, Tianxing He:
PCFG-Based Natural Language Interface Improves Generalization for Controlled Text Generation. *SEM@ACL 2023: 295-313 - [i24]Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
CooK: Empowering General-Purpose Language Models with Modular and Collaborative Knowledge. CoRR abs/2305.09955 (2023) - [i23]Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov:
Can Language Models Solve Graph Problems in Natural Language? CoRR abs/2305.10037 (2023) - [i22]Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Fatemehsadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov:
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud. CoRR abs/2309.17157 (2023) - [i21]Yike Wang, Shangbin Feng, Heng Wang, Weijia Shi, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
Resolving Knowledge Conflicts in Large Language Models. CoRR abs/2310.00935 (2023) - [i20]Wenxuan Ding, Shangbin Feng, Yuhan Liu, Zhaoxuan Tan, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov:
Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models. CoRR abs/2310.01290 (2023) - [i19]Abe Bohan Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov:
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation. CoRR abs/2310.03991 (2023) - [i18]Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, Tianxing He:
On the Zero-Shot Generalization of Machine-Generated Text Detectors. CoRR abs/2310.05165 (2023) - [i17]Yuyang Bai, Shangbin Feng, Vidhisha Balachandran, Zhaoxuan Tan, Shiqi Lou, Tianxing He, Yulia Tsvetkov:
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models. CoRR abs/2310.09725 (2023) - [i16]Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül:
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics. CoRR abs/2311.02258 (2023) - 2022
- [b1]Tianxing He:
Towards a Deeper Understanding of Neural Language Generation. MIT, USA, 2022 - [j3]Yilin Yang, Tianxing He, Yang Feng, Shaoying Liu, Baowen Xu:
Mining Python fix patterns via analyzing fine-grained source code changes. Empir. Softw. Eng. 27(2): 48 (2022) - [j2]Yilin Yang, Tianxing He, Zhilong Xia, Yang Feng:
A comprehensive empirical study on bug characteristics of deep learning frameworks. Inf. Softw. Technol. 151: 107004 (2022) - [j1]Rui Zhou, Hongchao Xu, Hao Zhang, Jie Zhang, Miao Liu, Tianxing He, Jun Gao, Chunlin Li:
Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai. Remote. Sens. 14(16): 4098 (2022) - [c18]Jiabao Ji, Yoon Kim, James R. Glass, Tianxing He:
Controlling the Focus of Pretrained Language Generation Models. ACL (Findings) 2022: 3291-3306 - [i15]Jiabao Ji, Yoon Kim, James R. Glass, Tianxing He:
Controlling the Focus of Pretrained Language Generation Models. CoRR abs/2203.01146 (2022) - [i14]Jingyu Zhang, James R. Glass, Tianxing He:
PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation. CoRR abs/2210.07431 (2022) - [i13]Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James R. Glass, Yulia Tsvetkov:
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation. CoRR abs/2212.10020 (2022) - 2021
- [c17]Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James R. Glass, Fuchun Peng:
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models. EACL 2021: 1121-1133 - [c16]Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl:
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models. EACL 2021: 1754-1761 - [c15]Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass:
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? EMNLP (1) 2021: 5087-5102 - [i12]Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl:
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models. CoRR abs/2101.06829 (2021) - [i11]Tianxing He, Kyunghyun Cho, James R. Glass:
An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models. CoRR abs/2109.02772 (2021) - [i10]Lu Mi, Tianxing He, Core Francisco Park, Hao Wang, Yue Wang, Nir Shavit:
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework. CoRR abs/2110.06421 (2021) - 2020
- [c14]Tianxing He, James R. Glass:
Negative Training for Neural Dialogue Response Generation. ACL 2020: 2044-2058 - [c13]Ke Li, Zhe Liu, Tianxing He, Hongzhao Huang, Fuchun Peng, Daniel Povey, Sanjeev Khudanpur:
An Empirical Study of Transformer-Based Neural Language Model Adaptation. ICASSP 2020: 7934-7938 - [c12]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. ICLR 2020 - [c11]Moin Nadeem, Tianxing He, Kyunghyun Cho, James R. Glass:
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation. AACL/IJCNLP 2020: 334-346 - [i9]Seunghak Yu, Tianxing He, James R. Glass:
Constructing a Knowledge Graph from Unstructured Documents without External Alignment. CoRR abs/2008.08995 (2020) - [i8]Moin Nadeem, Tianxing He, Kyunghyun Cho, James R. Glass:
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation. CoRR abs/2009.07243 (2020)
2010 – 2019
- 2019
- [c10]Tianxing He, James R. Glass:
Detecting Egregious Responses in Neural Sequence-to-sequence Models. ICLR (Poster) 2019 - [c9]Tianxing He, Shengcheng Yu, Ziyuan Wang, Jieqiong Li, Zhenyu Chen:
From Data Quality to Model Quality: An Exploratory Study on Deep Learning. Internetware 2019: 18:1-18:6 - [i7]Tianxing He, James R. Glass:
Negative Training for Neural Dialogue Response Generation. CoRR abs/1903.02134 (2019) - [i6]Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass:
Quantifying Exposure Bias for Neural Language Generation. CoRR abs/1905.10617 (2019) - [i5]Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie:
Analysis of Gradient Clipping and Adaptive Scaling with a Relaxed Smoothness Condition. CoRR abs/1905.11881 (2019) - [i4]Tianxing He, Shengcheng Yu, Ziyuan Wang, Jieqiong Li, Zhenyu Chen:
From Data Quality to Model Quality: an Exploratory Study on Deep Learning. CoRR abs/1906.11882 (2019) - [i3]Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James R. Glass, Fuchun Peng:
Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models. CoRR abs/1910.07117 (2019) - 2018
- [i2]Tianxing He, James R. Glass:
Detecting egregious responses in neural sequence-to-sequence models. CoRR abs/1809.04113 (2018) - 2017
- [c8]Yue Wu, Tianxing He, Zhehuai Chen, Yanmin Qian, Kai Yu:
Multi-view LSTM Language Model with Word-Synchronized Auxiliary Feature for LVCSR. CCL 2017: 398-410 - 2016
- [c7]Tianxing He, Jasha Droppo:
Exploiting LSTM structure in deep neural networks for speech recognition. ICASSP 2016: 5445-5449 - [c6]Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu:
On training bi-directional neural network language model with noise contrastive estimation. ISCSLP 2016: 1-5 - [i1]Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu:
On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation. CoRR abs/1602.06064 (2016) - 2015
- [c5]Yongbin You, Yanmin Qian, Tianxing He, Kai Yu:
An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition. ChinaSIP 2015: 30-34 - [c4]Tianxing He, Xu Xiang, Yanmin Qian, Kai Yu:
Recurrent neural network language model with structured word embeddings for speech recognition. ICASSP 2015: 5396-5400 - [c3]Yanmin Qian, Tianxing He, Wei Deng, Kai Yu:
Automatic model redundancy reduction for fast back-propagation for deep neural networks in speech recognition. IJCNN 2015: 1-6 - [c2]Wengong Jin, Tianxing He, Yanmin Qian, Kai Yu:
Paragraph vector based topic model for language model adaptation. INTERSPEECH 2015: 3516-3520 - 2014
- [c1]Tianxing He, Yuchen Fan, Yanmin Qian, Tian Tan, Kai Yu:
Reshaping deep neural network for fast decoding by node-pruning. ICASSP 2014: 245-249
Coauthor Index
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