Tianxing He (贺天行)

I'll join THU(清华) in Sep 2024. I plan to work on AI + simulation or gaming.

My UW email no longer works (July 1 2024)! Please use my gmail if you want to contact.

目前我想招一个本科research intern,做LLM社会模拟方向(如AI小镇),或者AI+游戏方向(比如minecraft agent或者游戏关卡生成)。需要有很强的自学能力和对科研项目的极大热情。有兴趣的同学请联系我。(一些paper例子:https://arxiv.org/abs/2302.05981,https://arxiv.org/pdf/2310.03903,https://arxiv.org/pdf/2403.19267,https://arxiv.org/pdf/2106.10155,https://arxiv.org/abs/2304.03442)

Not long ago I was a postdoc at UW, supervised by Yulia Tsvetkov, who runs the Tsvetshop. A while ago, I was a PhD student at MIT, supervised by Prof. James Glass, who runs the SLS group.

I did my bachelor and master degree at Shanghai Jiao Tong University, and my research there was supervised by Prof. Kai Yu, who runs the SJTU SpeechLab. At SJTU I was in the ACM honored class.

I was on academia job market in winter 2023 and spring 2024. Here are my CV and statements: CV, research, teaching, and diversity.

Recent research awards (mostly in 2023): The UW Postdoc Research Award, the CCF-Tencent Rhino-Bird Young Faculty Open Research Fund (with Yulia), and the ORACLE Project Award. The Best Paper Award at the Efficient Natural Language and Speech Processing Workshop (NeurIPS-ENLSP 2022).

Teaching: My guest lecture slides for UW NLP Course (undergrad/master level), Basics on NNLM(Back-propagation, RNN, etc.), and Advanced NNLM(attention, transformers, etc.). They are in this zip.

My partner and I raise two corgis Minnie&Mickey! We post their photos on RED , and Instagram .

I like to make fun videos with games, two of my favourite (most of them are in Chinese): (1) MarioKart at MIT. (2) I built a theme park for proposal.

CV  /  Email  /  Google Scholar  /  Twitter

profile photo

Interests: AI-related simulation, gaming, or safety. Most of my works during my PhD is focused on neural language generation.
Projects: Listed below. Representative papers are highlighted (they are also the projects I lead). * means co-first-author.

Towards Black-Box Membership Inference Attack for Diffusion Models
Jingwei Li, Jing Dong, Tianxing He, Jingzhao Zhang

We propose a surprisingly simple black-box MIA algorithm for diffusion models.

Learning Syntax without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically
Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A.Smith, Navin Goyal, Yulia Tsvetkov

We extensively experiment with transformer models trained on multiple synthetic datasets and with different training objectives and show that while other objectives e.g., sequence-to-sequence modeling, prefix language modeling, often failed to lead to hierarchical generalization, models trained with the language modeling objective consistently learned to generalize hierarchically.

MiniAgents: A Visualization Interface for Simulacra
Tianxing He
Demo project. Currently only Mac is supported.

I'm using the Unity game engine to build a visualization interface for simulacra. It's now functional and you are welcome to try it. It's still a work-in-progress.

Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Yichen Wang, Shangbin Feng, Abe Bohan Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, Tianxing He
ACL 2024

We comprehensively study the robustness of popular machine-generated text detectors under attacks from diverse categories: editing, paraphrasing, prompting, and co-generating.

On the Blind Spots of Model-Based Evaluation Metrics for Text Generation
Tianxing He*, Jingyu Zhang*, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, Yulia Tsvetkov
ACL 2023, selfcontained-oral-slide

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation.

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
Abe Bohan Hou*, Jingyu Zhang*, Tianxing He*, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
NAACL 2024

Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level semantic watermarking algorithm based on locality-sensitive hashing (LSH), which partitions the semantic space of sentences.

LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
Mengke Zhang*, Tianxing He*, Tianle Wang, Lu Mi, Fatemehsadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov
NAACL 2024 Findings

In the current user-server interaction paradigm for prompted generation, there is zero option for users who want to keep the generated text to themselves. We propose LatticeGen, a cooperative framework in which the server still handles most of the computation while the user controls the sampling operation. In the end, the server does not know what exactly is generated. The key idea is that the true generated sequence is mixed with noise tokens by the user and hidden in a noised lattice.

k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, Tianxing He
ACL-Findings 2024

We propose k-SemStamp, a simple yet effective enhancement of SemStamp, utilizing k-means clustering as an alternative of LSH to partition the embedding space with awareness of inherent semantic structure.

Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov
ICLR 2024
Reviewer Scores: 8/8/8

We propose Knowledge Card, a community-driven initiative to empower black-box LLMs with modular and collaborative knowledge. By incorporating the outputs of independently trained, small, and specialized LMs, we make LLMs better knowledge models by empowering them with temporal knowledge update, multi-domain knowledge synthesis, and continued improvement through collective efforts.

On the Zero-Shot Generalization of Machine-Generated Text Detectors
Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, Tianxing He
EMNLP-Findings 2023

How will the detectors of machine-generated text perform on outputs of a new generator, that the detectors were not trained on? We begin by collecting generation data from a wide range of LLMs, and train neural detectors on data from each generator and test its performance on held-out generators. While none of the detectors can generalize to all generators, we observe a consistent and interesting pattern that the detectors trained on data from a medium-size LLM can zero-shot generalize to the larger version.

Can Language Models Solve Graph Problems in Natural Language?
Heng Wang*, Shangbin Feng*, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov
NeurIPS 2023

Are language models graph reasoners? We propose the NLGraph benchmark, a test bed for graph-based reasoning designed for language models in natural language. We find that LLMs are preliminary graph thinkers while the most advanced graph reasoning tasks remain an open research question.

Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
Lu Mi*, Trung Le*, Tianxing He, Eli Shlizerman, Uygar Sümbül
NeurIPS 2023

We use implicit dynamical models to learn time-invariant representation for individual neurons from population dynamics and enable mapping functional activity to cell types.

PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation
Jingyu Zhang, James Glass, Tianxing He
The 2022 Efficient Natural Language and Speech Processing Workshop (NeurIPS ENLSP 2022)
The Best Paper Award at the Workshop
The 12th Joint Conference on Lexical and Computational Semantics (StarSEM 2023)

We propose a natural language (NL) interface for controlled text generation, where we craft a PCFG to embed the control attributes into natural language commands, and propose variants of existing CTG models that take commands as input.

Controlling the Focus of Pretrained Language Generation Models
Jiabao Ji, Yoon Kim, James Glass, Tianxing He
ACL-Findings 2022

Different focus in the context leads to different generation! We develop the "focus vector" method to control the focus of a pretrained language model.

Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?
Tianxing He, Jingzhao Zhang, Zhiming Zhou, James Glass
EMNLP 2021

By feeding the LM with different types of prefixes, we could assess how serious exposure bias is. Surprisingly, our experiments reveal that LM has the self-recovery ability, which we hypothesize to be countering the harmful effects from exposure bias.

Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
Tianxing He, Bryan McCann, Caiming Xiong, Ehsan Hosseini-Asl
EACL 2021

We explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. Our experiments show that EBM training can help the model reach a better calibration that is competitive to strong baselines, with little or no loss in accuracy.

An Empirical Study on Few-shot Knowledge Probing for Pretrained Language Models
Tianxing He, Kyunghyun Cho, James Glass
On Arxiv

We compare a variety of approaches under a few-shot knowledge probing setting, where only a small number (e.g., 10 or 20) of example triples are available. In addition, we create a new dataset named TREx-2p, which contains 2-hop relations.

Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, Fuchun Peng
EACL 2021

After finetuning of pretrained NLG models, does the model forget some precious skills learned pretraining? We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection.

A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation
Moin Nadeem*, Tianxing He* (equal contribution), Kyunghyun Cho, James Glass
AACL 2020

We identify a few interesting properties that are shared among existing sampling algorithms for NLG. We design experiments to check whether these properties are crucial for the good performance.

Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie
ICLR 2020
Reviewer Scores: 8/8/8

We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived from practical neural network training examples.

Negative Training for Neural Dialogue Response Generation
Tianxing He, James Glass
ACL 2020

Can we "correct" some detected bad behaviors of a NLG model? We use negative examples to feed negative training signals to the model.

AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering
Seunghak Yu, Tianxing He, James Glass

We propose a novel framework to automatically construct a KG from unstructured documents that does not require external alignment.

An Empirical Study of Transformer-based Neural Language Model Adaptation
Ke Li, Zhe Liu, Tianxing He, Hongzhao Huang, Fuchun Peng, Daniel Povey, Sanjeev Khudanpur

We propose a mixer of dynamically weighted LMs that are separately trained on source and target domains, aiming to improve simple linear interpolation with dynamic weighting.

Detecting Egregious Responses in Neural Sequence-to-sequence Models
Tianxing He, James Glass
ICLR 2019

Can we trick dialogue response models to emit dirty words?

On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu

We attempt to train a bi-directional RNNLM via noise contrastive estimation.

Exploiting LSTM Structure in Deep Neural Networks for Speech Recognition
Tianxing He, Jasha Droppo

We design a LSTM structure in the depth dimension, instead of its original use in time-step dimension.

Recurrent Neural Network Language Model with Structured Word Embeddings for Speech Recognition
Tianxing He, Xu Xiang, Yanmin Qian, Kai Yu

We restructure word embeddings in a RNNLM to take advantage of its sub-units.

Reshaping Deep Neural Network for Fast Decoding by Node-Pruning
Tianxing He, Yuchen Fan, Yanmin Qian, Tian Tan, Kai Yu

We prune neurons of a DNN for faster inference.

The design and code of this website is borrowed from Jon Barron's site.