Zhao, Xinran

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Dense X Retrieval: What Retrieval Granularity Should We Use?

Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 74

Dense X Retrieval: What Retrieval Granularity Should We Use?

Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 74

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree

Zhang, Y., Zhao, X., Wang, Z. Z., Yang, C., Wei, J., & Wu, T. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025

Software Engineering ResearchTopic Modeling

Cited by 11

Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation

Yang, C., Zhao, X., Wu, T., & Kästner, C. (2026) · arXiv

Artificial Intelligence ApplicationsNatural Language Processing Techniques

Cited by 11

Improving Large Language Model Planning with Action Sequence Similarity

Zhao, X., Sedghi, H., Bohnet, B., Schuurmans, D., & Nova, A. (2025) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 0