511 citations · 1.4k across the 31 of their papers we have counts for
31 papers
Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors
Fan Nie, Lan Feng, Haotian Ye +5
Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often imp…
The Widespread Adoption of Large Language Model-Assisted Writing Across Society
Weixin Liang, Yaohui Zhang, Mihai Codreanu +3
The recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-ass…
Adaptive Self-improvement LLM Agentic System for ML Library Development
Genghan Zhang, Weixin Liang, Olivia Hsu +1
ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing thes…
Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity
Weixin Liang, Junhong Shen, Genghan Zhang +3
State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling, but their inability to leverage modality-specific features limits their pe…
LMFusion: Adapting Pretrained Language Models for Multimodal Generation
Weijia Shi, Xiaochuang Han, Chunting Zhou +4
We present LMFusion, a framework for empowering pretrained text-only large language models (LLMs) with multimodal generative capabilities, enabling them to understand and generate…
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
Weixin Liang, Lili Yu, Liang Luo +8
The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models…
Automated radiotherapy treatment planning guided by GPT-4Vision
Sheng Liu, Oscar Pastor-Serrano, Yizheng Chen +10
Objective: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple con…
Mapping the Increasing Use of LLMs in Scientific Papers
Weixin Liang, Yaohui Zhang, Zhengxuan Wu +11
Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledg…
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
Weixin Liang, Zachary Izzo, Yaohui Zhang +9
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). Our maximum l…
What's documented in AI? Systematic Analysis of 32K AI Model Cards
Weixin Liang, Nazneen Rajani, Xinyu Yang +5
The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in var…
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face
Xinyu Yang, Weixin Liang, James Zou
Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and trans…
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao +9
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scient…
OpenDataVal: a Unified Benchmark for Data Valuation
Kevin Fu Jiang, Weixin Liang, James Zou +1
Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data v…
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Weixin Liang, Yining Mao, Yongchan Kwon +2
Understanding the performance of machine learning (ML) models across diverse data distributions is critically important for reliable applications. Despite recent empirical studies…
GPT detectors are biased against non-native English writers
Weixin Liang, Mert Yuksekgonul, Yining Mao +2
The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential…
SEAL : Interactive Tool for Systematic Error Analysis and Labeling
Nazneen Rajani, Weixin Liang, Lingjiao Chen +2
With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these m…
Data Budgeting for Machine Learning
Xinyi Zhao, Weixin Liang, James Zou
Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practi…
GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language
Zhiying Zhu, Weixin Liang, James Zou
Helping end users comprehend the abstract distribution shifts can greatly facilitate AI deployment. Motivated by this, we propose a novel task, dataset explanation. Given two image…
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
Roxana Daneshjou, Kailas Vodrahalli, Roberto A Novoa +16
Access to dermatological care is a major issue, with an estimated 3 billion people lacking access to care globally. Artificial intelligence (AI) may aid in triaging skin diseases.…
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon +2
We present modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models. Specifically, we show that different data modalities (e.g. images and…
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
Weixin Liang, James Zou
Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing…
Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao, Yu Wang, Sai Li +4
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world app…
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
Roxana Daneshjou, Kailas Vodrahalli, Weixin Liang +14
More than 3 billion people lack access to care for skin disease. AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of…
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations
Weixin Liang, Kai-Hui Liang, Zhou Yu
Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating…