Cathy Jiao

Email: cljiao@cs.cmu.edu

I am a PhD student at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University, advised by Chenyan Xiong. Recently, I spent a wonderful summer in NYC at Spotify Research, hosted by Paul Bennett.

My research focuses on data-centric AI: designing methods/frameworks to better understand, curate, and evaluate the data for large language models. A central thread of my work is data attribution – identifying how individual data points shape model outputs – which I explore through efficient approximations and practical applications such as dataset curation and data valuation/pricing. More broadly, I aim to develop frameworks that make data usage more transparent, reliable, and impactful for both research and deployment.

Previously, I finished my masters at CMU LTI where I was advised by Maxine Eskenazi and Aaron Steinfeld. Before grad school, I spent time in industry working on machine learning and deep learning applications for natural language processing. Prior to that, I graduated with distinction from the University of British Columbia with a B.S. in CS & Math.

In my spare time, I enjoy cooking and biking around Pittsburgh. If you work on similar topics or want to chat, feel free to reach out!

Selected Publications

(See Google Scholar for all)

*= equal contribution

DATE-LM: Benchmarking Data Attribution Evaluation for Large Language Models

Cathy Jiao*, Yijun Pan*, Emily Xiao*, Daisy Sheng, Niket Jain, Hanzhang Zhao, Ishita Dasgupta, Jiaqi W. Ma, and Chenyan Xiong

NeurIPS 2025

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Data attribution methods quantify the influence of training data on model outputs and are becoming increasingly relevant for a wide range of LLM research and applications, including dataset curation, model interpretability, data valuation. However, there remain critical gaps in systematic LLM-centric evaluation of data attribution methods. To this end, we introduce DATE-LM (Data Attribution Evaluation in Language Models), a unified benchmark for evaluating data attribution methods through real-world LLM applications. DATE-LM measures attribution quality through three key tasks – training data selection, toxicity/bias filtering, and factual attribution. Our benchmark is designed for ease of use, enabling researchers to configure and run large-scale evaluations across diverse tasks and LLM architectures. Furthermore, we use DATE-LM to conduct a large-scale evaluation of existing data attribution methods. Our findings show that no single method dominates across all tasks, data attribution methods have trade-offs with simpler baselines, and method performance is sensitive to task-specific evaluation design. Finally, we release a public leaderboard for quick comparison of methods and to facilitate community engagement. We hope DATE-LM serves as a foundation for future data attribution research in LLMs.
Fairshare Data Pricing for Large Language Models

Luyang Zhang*, Cathy Jiao*, Beibei Li, and Chenyan Xiong

NeurIPS 2025

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Training data is a pivotal resource for building large language models (LLMs), but unfair pricing in data markets poses a serious challenge for both data buyers (e.g., LLM builders) and sellers (e.g., human annotators), which discourages market participation, reducing data quantity and quality. In this paper, we propose a fairshare pricing framework that sets training data prices using data valuation methods to quantify their contribution to LLMs. In our framework, buyers make purchasing decisions using data valuation and sellers set prices to maximize their profits based on the anticipated buyer purchases. We theoretically show that pricing derived from our framework is tightly linked to data valuation and buyers’ budget, optimal for both buyers and sellers. Through market simulations using current LLMs and datasets (math problems, medical diagnosis, and physical reasoning), we show that our framework is fairshare for buyers by ensuring their purchased data is reflective of model training value, leading to higher LLM task performances per-dollar spent on data, and fairshare for sellers by ensuring they sell their data at optimal prices. Our framework lays the foundation for future research on equitable and sustainable data markets for large-scale AI.
On the Feasibility of In-Context Probing for Data Attribution

Cathy Jiao, Gary Gao, Aditi Raghunathan, and Chenyan Xiong

NAACL 2025

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Data attribution methods are used to measure the contribution of training data towards model outputs, and have several important applications in areas such as dataset curation and model interpretability. However, many standard data attribution methods, such as influence functions, utilize model gradients and are computationally expensive. In our paper, we show in-context probing (ICP) – prompting a LLM – can serve as a fast proxy for gradient-based data attribution for data selection under conditions contingent on data similarity. We study this connection empirically on standard NLP tasks, and show that ICP and gradient-based data attribution are well-correlated in identifying influential training data for tasks that share similar task type and content as the training data. Additionally, fine-tuning models on influential data selected by both methods achieves comparable downstream performance, further emphasizing their similarities. We also examine the connection between ICP and gradient-based data attribution using synthetic data on linear regression tasks. Our synthetic data experiments show similar results with those from NLP tasks, suggesting that this connection can be isolated in simpler settings, which offers a pathway to bridging their differences.
A Survey of Data Attribution: Methods, Applications, and Evaluation in the Era of Generative AI

Junwei Deng, Yuzheng Hu, Pingbang Hu, Ting-Wei Li, Shixuan Liu, Jiachen T Wang, Dan Ley, Qirun Dai, Benhao Huang, Jin Huang, and others

2025

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Training data is the fuel of modern artificial intelligence (AI), fundamentally shaping the capabilities, limitations, and biases of AI systems. The emergence of large-scale generative models has elevated the importance of understanding how data influences their behaviors, bringing the field of data attribution to the forefront. This survey provides a comprehensive overview of data attribution, covering its methods, applications, and evaluation protocols, with a particular emphasis on the challenges and opportunities arising in the era of generative AI. We start by introducing a conceptual framework for attribution centered on three core questions: what to attribute (model behaviors), attribute to what (training entities), and how to attribute (influence measures). Within this framework, we systematically review major attribution approaches, including those based on influence functions, weighted marginal contributions, training dynamics, and simulators. We then examine key applications of data attribution, such as data selection, fact tracing, adversarial attacks and defenses, and the emerging data economy. Finally, we critically assess common evaluation criteria, including the quality of counterfactual predictions, utility in downstream tasks, and computational efficiency. We conclude with a forward-looking perspective on the future of data attribution, highlighting key open challenges and promising directions for future research.
Understanding the Effectiveness of Very Large Language Models on Dialog Evaluation

Jessica Huynh, Cathy Jiao, Prakhar Gupta, Shikib Mehri, Payal Bajaj, Vishrav Chaudhary, and Maxine Eskenazi

IWSDS 2023

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Language models have steadily increased in size over the past few years. They achieve a high level of performance on various natural language processing (NLP) tasks such as question answering and summarization. Large language models (LLMs) have been used for generation and can now output human-like text. Due to this, there are other downstream tasks in the realm of dialog that can now harness the LLMs’ language understanding capabilities. Dialog evaluation is one task that this paper will explore. It concentrates on prompting with LLMs: BLOOM, OPT, GPT-3, Flan-T5, InstructDial and TNLGv2. The paper shows that the choice of datasets used for training a model contributes to how well it performs on a task as well as on how the prompt should be structured. Specifically, the more diverse and relevant the group of datasets that a model is trained on, the better dialog evaluation performs. This paper also investigates how the number of examples in the prompt and the type of example selection used affect the model’s performance.
Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, and Jeffrey P. Bigham

EMNLP 2022

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Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.