Chao Zhang
Edenfield Early-Career Associate Professor
Researches label-efficient text mining, knowledge extraction and natural language processing with large language models.
Chao Zhang's lab at Georgia Tech works on large language models, knowledge, QA & retrieval and efficient training & inference. 66 papers since 2023, 51 at top venues, mostly ACL and NeurIPS. 9 PhD students. 9 alumni, most often to Amazon and Building a startup in Fintech. Applications close .
At a glance
- papers, 2023–2026
- 17/ year
- at T1 venues
- 77%
- PhD students
- 9
- MS students
- 0
- alumni listed
- 9
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 2 · collecting
- 14 papers2025 · T1 11
- 24 papers2024 · T1 18
- 25 papers2023 · T1 20
T1 venuesOtherVenues: ACL, NeurIPS, ICML, KDD, NAACL
Research focuspaper titles that use each topic's words
- Large language models34 papers
- Knowledge, QA & retrieval8 papers
- Efficient training & inference8 papers
- extraction4 papers
- text mining0 papers
Paper list
From the lab homepage.
20263 papers
- ICLR (T1)MLE-Smith: Scaling MLE Tasks with Automated Multi-agent Pipeline (opens in a new tab)
- ICML (T1)Towards Diverse Scientific Hypothesis Search with Large Language Models (opens in a new tab)
- EACLWorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning (opens in a new tab)
202514 papers
- NAACL (T1)Adapting LLM Agents with Universal Communication Feedback (opens in a new tab)
- ACL (T1)AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs (opens in a new tab)
- KDD (T1)Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement (opens in a new tab)
- AISTATS (T1)Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints (opens in a new tab)
- EMNLP-FindingsDORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (opens in a new tab)
- ICLR (T1)Efficient Evolutionary Search Over Chemical Space with Large Language Models (opens in a new tab)
- ICLR (T1)Ensembles of Low-Rank Expert Adapters (opens in a new tab)
- ICML (T1)Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang
- NAACL (T1)Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (opens in a new tab)
- COLMLanguage Model Uncertainty Quantification with Attention Chain (opens in a new tab)
- NeurIPS (T1)MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering (opens in a new tab)
- NAACL (T1)Self-Generated Critiques Boost Reward Modeling for Language Models (opens in a new tab)
202424 papers
- EMNLP-FindingsA Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction (opens in a new tab)
- Chemistry of MaterialsAccelerating Materials Discovery for Polymer Solar Cells: Data-driven Insights Enabled by Natural Language Processing (opens in a new tab)
- NeurIPS (T1)Aligning Large Language Models with Representation Editing: A Control Perspective (opens in a new tab)
- ACL (T1)ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (opens in a new tab)
- NAACL (T1)Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study (opens in a new tab)
- ICML (T1)BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models (opens in a new tab)
- EMNLP (T1)BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (opens in a new tab)
- RECOMBContrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape (opens in a new tab)
- EMNLP-FindingsData Diversity Matters for Robust Instruction Tuning (opens in a new tab)
- ACL (T1)Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (opens in a new tab)
- EACLHiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (opens in a new tab)
- NeurIPS (T1)HYDRA: Model Factorization Framework for Black-Box LLM Personalization (opens in a new tab)
202325 papers
- npj Comput MaterialsA General-Purpose Material Property Data Extraction Pipeline from Large Polymer Corpora Using Natural Language Processing (opens in a new tab)
- Journal of Physical Chemistry AAccelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning (opens in a new tab)
- NeurIPS (T1)AdaPlanner: Adaptive Planning from Feedback with Language Models (opens in a new tab)
- ICML (T1)Autoregressive Diffusion Model for Graph Generation (opens in a new tab)
- ACL (T1)Cold-start Data Selection for Better Few-shot Fine-tuning of Pretrained Language Models (opens in a new tab)
- ACL (T1)Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites (opens in a new tab)
- KDD (T1)DyGen: Fine-Tuning Language Models with Noisy Labels by Dynamics-Enhanced Generative Modeling (opens in a new tab)
- ACL (T1)Extracting Shopping Interest-Related Product Types from the Web (opens in a new tab)
- CIKM (T1)G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer (opens in a new tab)
- ACL (T1)Graph Reasoning for Question Answering with Triplet Retrieval (opens in a new tab)
- EMNLP-FindingsImproving Consistency for Text Summarization with Energy Functions (opens in a new tab)
- EMNLP-FindingsKnowledge-Selective Pretraining for Attribute Value Extraction (opens in a new tab)
Where alumni went9 listed, 89% to industry
Industry 8Academia 1
Most often to
- Amazon
- Building a startup in Fintech
- Citadel
Also to
- Harvard University
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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