Zongyu (Johnson) Lin「林宗裕」

I am a first-year CS Ph.D Student at UCLA, co-advised by Prof. Yizhou Sun and Prof. Kaiwei Chang. Before coming to UCLA, I have spent a year in Moonshot.AI (one of the earliest core members), working as a full-time LLM researcher. I was one of the major contributor of training large language models with extremely long context, achieving the state-of-the-art performance on many long context tasks compared with GPT4 and Claude2, and also participated in visual generation project. I completed my bachelor's degree of Electronic Enginnering at Tsinghua University. Luckily, I have worked with Prof. Zhilin Yang, Prof. Yong Li, Prof. Hanan Samet and Prof. Cyrus Shahabi.

My research interest lies broadly in natural language processing and general machine learning. I have done some work including self-training, instruction finetuning and zero-shot task generalization of LLMs. Most Recently, I am interested in (1) studying the self-evolution paradigm of large language models as well as (2) exploring scalable architectures and recipes for multi-modal generation. Feel free to contact me for chat or discussion if you are also interested in these topics.

Email: lzyxx17 [at] gmail.com

Twitter   Google Scholar   Linkedin

My research interest lies broadly in natural language processing and general machine learning. Most Recently, I am interested in

(1) improving the self-evolution of large language models;

(2) exploring scalable architectures and recipes for multi-modal generation;

(3) improve better alignment with the physical world for vision / embodied language models

Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact checking and data cleaning. To retrieve contradiction argument to the query from large document corpora, existing methods such as similarity search and crossencoder models exhibit significant limitations. The former struggles to capture the essence of contradiction due to its inherent nature of favoring similarity, while the latter suffers from computational inefficiency, especially when the size of corpora is large. To address these challenges, we introduce a novel approach: SPARSECL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. Our method utilizes a combined metric of cosine similarity and a sparsity function to efficiently identify and retrieve documents that contradict a given query. This approach dramatically enhances the speed of contradiction detection by reducing the need for exhaustive document comparisons to simple vector calculations. We validate our model using the Arguana dataset, a benchmark dataset specifically geared towards contradiction retrieval, as well as synthetic contradictions generated from the MSMARCO and HotpotQA datasets using GPT-4. Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval. This paper outlines a promising direction for improving the accuracy and efficiency of contradiction retrieval in large-scale text corpora.

Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts and styles. Due to their ability to synthesize realistic motions and render complex objects, these generative models have the potential to become general-purpose simulators of the physical world. However, it is unclear how far we are from this goal with the existing text-to-video generative models. To this end, we present VideoPhy, a benchmark designed to assess whether the generated videos follow physical commonsense for real-world activities (e.g. marbles will roll down when placed on a slanted surface). Specifically, we curate a list of 688 captions that involve interactions between various material types in the physical world (e.g., solid-solid, solid-fluid, fluid-fluid). We then generate videos conditioned on these captions from diverse state-of-the-art text-to-video generative models, including open models (e.g., VideoCrafter2) and closed models (e.g., Lumiere from Google, Pika). Further, our human evaluation reveals that the existing models severely lack the ability to generate videos adhering to the given text prompts, while also lack physical commonsense. Specifically, the best performing model, Pika, generates videos that adhere to the caption and physical laws for only 19.7% of the instances. VideoPhy thus highlights that the video generative models are far from accurately simulating the physical world. Finally, we also supplement the dataset with an auto-evaluator, VideoCon-Physics, to assess semantic adherence and physical commonsense at scale.

Generative modeling has been the dominant approach for large-scale pretraining and zero-shot generalization. In this work, we challenge this convention by showing that discriminative approaches perform substantially better than generative ones on a large number of NLP tasks. Technically, we train a single discriminator to predict whether a text sample comes from the true data distribution, similar to GANs. Since many NLP tasks can be formulated as selecting from a few options, we use this discriminator to predict the option with the highest probability. This simple formulation achieves state-of-the-art zero-shot results on the T0 benchmark, outperforming T0 by 16.0%, 7.8%, and 11.5% respectively on different scales. Meanwhile, our approach requires minimal prompting efforts, which largely improves robustness and is essential for real-world applications.

Recent work has achieved remarkable zero-shot performance with multi-task prompted pretraining, but little has been understood. For the first time, we show that training on a small number of key tasks beats using all the training tasks, while removing these key tasks substantially hurts performance. We also find that these key tasks are mostly question answering (QA) tasks. We design a shuffle experiment to further show that training on these QA tasks leads to better cross-task generalization in multi-task learning under various training/test task splits. These novel findings combined deepen our understanding about zero-generalization— training on certain tasks such as QA encodes general knowledge transferable to a wide range of tasks, which explains the improved zero-shot performance in recent progress. In addition, to automate this procedure, we devise a method to identify and upsample key training tasks without observing the test tasks based on cross validation. Empirically, our approach achieves improved results across various model scales and tasks.

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose SENT (Selection-Enhanced Noisy label Training) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose SENT (Selection-Enhanced Noisy label Training) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.

We propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns.

We propose a general system to recover vehicle trajectories at the level of the road intersection, where a novel iterative framework is developed to combine both vehicle clustering and trajectory recovery tasks.

We propose a DFA-based model which can generate interpretable features automatically from raw mobility data for fine-grained health sensing.

We propose a semantic-enhanced urban mobility embedding model for user profiling, and reveal meaningful patterns in all spatial, temporal and urban structure domains.

We introduce a new end-to-end spatiotemporal learning framework dubbed CrimeForecaster that: 1) represents the geographical extents of neighborhoods and their correlations in a graph; 2) uses graph convolution to predict crimes.

LLM Researcher, Moonshot.AI 2023

Quant Researcher Intern, Ubiquant, Top Hedge Fund in China. 2022

Research Intern, Sensetime, China, 2021

Comprehensive Outstanding Scholarship(~10/280), Tsinghua University. 2020

Excellent Technology Innovation Scholarship, Tsinghua University. 2020

First Prize in Software Design Contest, Department of Electronic Enginnering, Tsinghua University. 2018

Sports! I really enjoy playing ballgames like football and tennis. I am a big fan of Lionel Messi, Rafael Nadal and Stephen Curry! Also, I love running, swimming and hiking.