Neubig, Graham

Weakly- and Semi-supervised Evidence Extraction

Pruthi, D., Dhingra, B., Neubig, G., & Lipton, Z. C. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 13

Paraphrastic Representations at Scale

Wieting, J., Gimpel, K., Neubig, G., & Berg-kirkpatrick, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Topic ModelingNatural Language Processing Techniques

Cited by 13

Learning to Filter Context for Retrieval-Augmented Generation

Wang, Z., Araki, J., Jiang, Z., Parvez, M. R., & Neubig, G. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

EXCALIBUR: Encouraging and Evaluating Embodied Exploration

Zhu, H., Kapoor, R., Min, S. Y., Han, W., Li, J., Geng, K., Neubig, G., Bisk, Y., Kembhavi, A., & Weihs, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 9

Beyond Browsing: API-Based Web Agents

Song, Y., Xu, F., Zhou, S., & Neubig, G. (2024) · arXiv

Web Data Mining and AnalysisInformation Retrieval and Search Behavior

Cited by 8

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Kim, S., Suk, J., Longpre, S., Lin, B. Y., Shin, J., Welleck, S., Neubig, G., Lee, M., Lee, K., & Seo, M. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

In-Context Learning with Long-Context Models: An In-Depth Exploration

Bertsch, A., Ivgi, M., Xiao, E., Alon, U., Berant, J., Gormley, M. R., & Neubig, G. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 7

Agent Workflow Memory

Wang, Z. Z., Mao, J., Fried, D., & Neubig, G. (2024) · arXiv

Web Data Mining and AnalysisInformation Retrieval and Search Behavior

Cited by 5

MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Yue, X., Zheng, T., Ni, Y., Wang, Y., Zhang, K., Tong, S., Sun, Y., Yu, B., Zhang, G., Sun, H., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 4

Demystifying Long Chain-of-Thought Reasoning in LLMs

Yeo, E., Tong, Y., Niu, M., Neubig, G., & Yue, X. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

Instruction-tuned Language Models are Better Knowledge Learners

Jiang, Z., Sun, Z., Shi, W., Rodriguez, P., Zhou, C., Neubig, G., Lin, X. V., Yih, W.-T., & Iyer, S. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 3

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Welleck, S., Bertsch, A., Finlayson, M., Schoelkopf, H., Xie, A., Neubig, G., Kulikov, I., & Harchaoui, Z. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 3

MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures

Ni, J., Xue, F., Yue, X., Deng, Y., Shah, M., Jain, K., Neubig, G., & You, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

Kim, S., Yoon, D., Gashteovski, K., Suk, J., Baek, J., Aggarwal, P., Wu, I., Zaverkin, V., Petkoski, S., Schrider, D. R., et al. (2026) · arXiv

Academic Writing and PublishingAcademic Publishing and Open Access

Cited by 1

VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks

Koh, J. Y., Lo, R., Jang, L., Duvvur, V., Lim, M. C., Huang, P.-Y., Neubig, G., Zhou, S., Salakhutdinov, R., & Fried, D. (2024) · arXiv

Multimodal Machine Learning ApplicationsWeb Data Mining and Analysis

Cited by 1

Humans are Missing from AI Coding Agent Research

Wang, Z. Z., Yang, J., Lieret, K., Tartaglini, A., Chen, V., Wei, Y., Wang, Z., Zhang, L., Narasimhan, K., Schmidt, L., et al. (2026) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 0

TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks

Wang, Z., Fried, D., & Neubig, G. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 0

Inducing Programmatic Skills for Agentic Tasks

Wang, Z. Z., Gandhi, A., Neubig, G., & Fried, D. (2025) · arXiv

Web Data Mining and AnalysisAI-based Problem Solving and Planning

Cited by 0