Junhong Shen

Ph.D. Student

Machine Learning Department
Carnegie Mellon University
Advisor: Ameet Talwalkar

Email: junhongs@andrew.cmu.edu
Office: Gates Hillman Centers 8226

Google Scholar   GitHub   Twitter

Bio

I’m a 4th-year Ph.D. student in the Machine Learning Department at CMU, advised by Ameet Talwalkar. My work centers on enhancing LLM’s interaction with real-world applications, in particular building multi-modal models and agent systems that operate in real-world environments, such as browsers, command lines, and IDEs. I’m also interested in enhancing LLMs’ abilities to model diverse data types and applying them to long-tail, low-resource domains such as science and business.

I obtained my B.S. in Mathematics of Computation at UCLA, where I was fortunate to work with Lin Yang on sample-efficient reinforcement learning. I have also worked on multi-agent RL and Theory of Mind, advised by Song-Chun Zhu and Ying Nian Wu. My PhD is supported by JP Morgan AI PhD Fellowship.

News

• Feb 2025: I'll intern at Google DeepMind (Mountain View) this May. Catch me up for a coffee!
• Jan 2025: Check out our new work on Multi-Modal Mixture of Mamba!
• Dec 2024: My internship work at Meta FAIR, Content-Adaptive Image Tokenizer, is released!
• Nov 2024: Check out our newest work on web agents built on top of open-source LLMs! ScribeAgent paper, code, and blog post.
• Sep 2024: Grateful to be awarded the J.P. Morgan AI PhD Fellowship (accepted) and the Bloomberg PhD Fellowship (declined)!
• May 2024: My internship work at Microsoft Research, Tag-LLM, is accepted by ICML 2024!
• May 2024: We are organizing the CMU Agent Workshop. Check out and participate!

Selected Publications

Junhong Shen, Kushal Tirumala, Michihiro Yasunaga, Ishan Misra, Luke Zettlemoyer, Lili Yu, Chunting Zhou
CAT: Content-Adaptive Image Tokenization

Preprint

Most existing image tokenizers encode images into a fixed number of tokens or patches. We introduce Content-Adaptive Tokenizer (CAT), which dynamically adjusts representation capacity based on the image content and encodes simpler images into fewer tokens. We design a caption-based evaluation system that leverages LLMs to predict content complexity and determine the optimal compression ratio for a given image.

papercite

Junhong Shen, Atishay Jain, Zedian Xiao, Ishan Amlekar, Mouad Hadji, Aaron Podolny, Ameet Talwalkar
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data

Preprint

Most LLM-based web agents rely on prompting general-purpose, proprietary models like GPT-4. We explore an alternative approach that fine-tunes open-source LLMs using production-scale workflow data. This simple yet effective approach achieves SOTA direct generation performance on Mind2Web and improves the task success rate by 7.3% over the previous best text-only web agents on WebArena.

papercodecite

Junhong Shen, Tanya Marwah, Ameet Talwalkar
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation

In TMLR 2024 & ICML AI4Science Workshop, 2024 (Spotlight).

UPS is developed for solving diverse spatiotemporal PDEs defined over various domains, dimensions, and resolutions. It unifies different PDEs into a consistent representation space and processes diverse collections of PDE data using a unified network architecture that combines LLMs with domain-specific neural operators.

papercodecite

Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolo Fusi
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

In ICML, 2024.

LLMs demonstrate proficiency in understanding natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and biomedical sciences. This work explores how to repurpose general LLMs into specialized task solvers through learning custom input tags to condition the LLM.

papercodecite

Junhong Shen, Liam Li, Lucio Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar
Cross-Modal Fine-Tuning: Align then Refine

In ICML, 2023 (Oral).

ORCA is a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. It adapts to a target task via an align-then-refine workflow. Given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretraining modality. The pretrained model is then fine-tuned on the embedded data to exploit the knowledge shared across modalities.

papercodetalktweetcite

Junhong Shen*, Mikhail Khodak*, Ameet Talwalkar
Efficient Architecture Search for Diverse Tasks

In NeurIPS, 2022.

DASH is developed for efficiently solving diverse ML problems outside of the well-researched domains such as vision and natural language processing. Being fast, simple, and broadly applicable, DASH fixes a standard CNN topology and searches for the right kernel sizes and dilation rates that its operations should take on. It expands the network capacity to extract features at multiple resolutions for different types of data while only requiring searching over the operation space.

papercodeblogpostcite

Renbo Tu*, Nicholas Roberts*, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks

In NeurIPS Datasets and Benchmarks Track, 2022.

Neural architecture search (NAS) benchmarks and methods prioritize performance on well-studied tasks, e.g., image classification on CIFAR and ImageNet. To mitigate this bias, NAS-Bench-360 is a benchmark suite for evaluating state-of-the-art NAS methods on a diverse set of tasks. The selection spans different application domains, dataset sizes, problem dimensionalities, and learning objectives.

papercodewebsiteblogpostcite

Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
Iterative Teacher-Aware Learning

In NeurIPS, 2021.

In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher’s cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works.

papercodecite

Junhong Shen, Lin F. Yang
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation

In AAAI, 2021.

We propose a theoretically principled nearest neighbor (NN) function approximator that can replace the value networks in deep RL methods. Inspired by human similarity judgments, the NN approximator estimates the action values using rollouts on past observations and can provably obtain a small regret bound that depends only on the intrinsic complexity of the environment.

papercodeslidescite

Luyao Yuan, Zipeng Fu, Jingyue Shen, Lu Xu, Junhong Shen, Song-Chun Zhu
Emergence of Pragmatics from Referential Game between Theory of Mind Agents

In Emergent Communication Workshop, NeurIPS, 2019.

We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.

paperwebsitecite

@misc{shen2024adaptivetokenizer,
 title={CAT: Content-Adaptive Image Tokenization},
 author={Junhong Shen and Kushal Tirumala and Michihiro Yasunaga and Ishan Misra and Luke Zettlemoyer and Lili Yu and Chunting Zhou},
 year={2025},
 eprint={2501.03120},
 archivePrefix={arXiv},
 primaryClass={cs.CV},
 }

@misc{shen2024scribeagent,
 title={ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data},
 author={Junhong Shen and Atishay Jain and Zedian Xiao and Ishan Amlekar and Mouad Hadji and Aaron Podolny and Ameet Talwalkar},
 year={2024},
 eprint={2411.15004},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 }

@misc{shen2024ups, title={UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation},
 author={Junhong Shen and Tanya Marwah and Ameet Talwalkar},
 year={2024},
 eprint={2403.07187},
 archivePrefix={arXiv},
 primaryClass={cs.LG}
 }

@misc{shen2024tagllm,
 title={Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains}, 
 author={Junhong Shen and Neil Tenenholtz and James Brian Hall and David Alvarez-Melis and Nicolo Fusi},
 year={2024},
 eprint={2402.05140},
 archivePrefix={arXiv},
 primaryClass={cs.LG}
 }

@misc{shen2023orca,
 author = {Shen, Junhong and Li, Liam and Dery, Lucio M. and Staten, Corey and Khodak, Mikhail and Neubig, Graham and Talwalkar, Ameet},
 title = {Cross-Modal Fine-Tuning: Align then Refine},
 publisher = {ICML},
 year = {2023},
 url = {https://arxiv.org/abs/2302.05738}
 }

@inproceedings{shen2022efficient,
 title={Efficient Architecture Search for Diverse Tasks},
 author={Shen, Junhong and Khodak, Mikhail and Talwalkar, Ameet},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
 year={2022}
 }

@inproceedings{nasbench360,
 title={NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks},
 author={Renbo Tu and Nicholas Roberts and Mikhail Khodak and Junhong Shen and Frederic Sala and Ameet Talwalkar},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track},
 year={2022}
 }

@inproceedings{yuan2021iterative,
 title={Iterative Teacher-Aware Learning},
 author={Luyao Yuan and Dongruo Zhou and Junhong Shen and Jingdong Gao and Jeffrey L. Chen and Quanquan Gu and Ying Nian Wu and Song-Chun Zhu},
 booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
 year={2021}
 }

@inproceedings{Shen2021TheoreticallyPD,
 title={Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation},
 author={Junhong Shen and Lin F. Yang},
 booktitle={AAAI},
 year={2021}
 }

@article{Yuan2020EmergenceOP,
 title={Emergence of Pragmatics from Referential Game between Theory of Mind Agents},
 author={Luyao Yuan and Zipeng Fu and Jingyue Shen and Lu Xu and Junhong Shen and Song-Chun Zhu},
 journal={NeurIPS 2019 Workshop on Emergent Communication},
 year={2019}
 }