Callison-Burch, Chris

Moses: open source toolkit for statistical machine translation

Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07

Natural Language Processing TechniquesSpeech Recognition and Synthesis

Cited by 4,735

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

A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk

Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., & Bigham, J. P. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Mobile Crowdsensing and CrowdsourcingDigital Economy and Work Transformation

Cited by 497

Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk

Callison-Burch, C. (2009) · Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 1 - EMNLP '09

Natural Language Processing TechniquesTranslation Studies and Practices

Cited by 412

Deduplicating Training Data Makes Language Models Better

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 304

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 264

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 264

Faithful Chain-of-Thought Reasoning

Lyu, Q., Havaldar, S., Stein, A., Zhang, L., Rao, D., Wong, E., Apidianaki, M., & Callison-Burch, C. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 201

Artificial Intelligence in mental health and the biases of language based models

Straw, I., & Callison-Burch, C. (2020) · PLOS ONE

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 164

Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims

Chen, S., Khashabi, D., Yin, W., Callison-Burch, C., & Roth, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 104

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yang, Y., Sun, F.-Y., Weihs, L., VanderBilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2023) · arXiv

3D Shape Modeling and AnalysisArtificial Intelligence in Games

Cited by 101

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yang, Y., Sun, F.-Y., Weihs, L., Vanderbilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisArtificial Intelligence in Games

Cited by 101

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Real or fake text?: Investigating human ability to detect boundaries between human-written and machine-generated text

Dugan, L., Ippolito, D., Kirubarajan, A., Shi, S., & Callison-Burch, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 54

RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors

Dugan, L., Hwang, A., Trhlík, F., Zhu, A., Ludan, J. M., Xu, H., Ippolito, D., & Callison-Burch, C. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 48

CALYPSO: LLMs as Dungeon Master's Assistants

Zhu, A., Martin, L., Head, A., & Callison-Burch, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 31

Faithful Chain-of-Thought Reasoning

Lyu, Q., Havaldar, S., Stein, A., Zhang, L., Rao, D., Wong, E., Apidianaki, M., & Callison-Burch, C. (2023) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 23

Calibrating Large Language Models with Sample Consistency

Lyu, Q., Shridhar, K., Malaviya, C., Zhang, L., Elazar, Y., Tandon, N., Apidianaki, M., Sachan, M., & Callison-Burch, C. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 21

Deduplicating Training Data Makes Language Models Better

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Learning Interpretable Style Embeddings via Prompting LLMs

Patel, A., Rao, D., Kothary, A., McKeown, K., & Callison-Burch, C. (2023) · arXiv

Authorship Attribution and ProfilingTopic Modeling

Cited by 10

Autorubric: A Unifying Framework for Rubric-Based LLM Evaluation on Non-Verifiable Tasks

Rao, D., & Callison-Burch, C. (2026) · arXiv

Student Assessment and FeedbackTopic Modeling

Cited by 7

Choice-75: A Dataset on Decision Branching in Script Learning

Hou, Z. J., Zhang, L., & Callison-Burch, C. (2023) · arXiv

Artificial Intelligence in GamesTeaching and Learning Programming

Cited by 5

WithdrarXiv: A Large-Scale Dataset for Retraction Study

Rao, D., Young, J., Dietterich, T., & Callison-Burch, C. (2024) · arXiv

Academic integrity and plagiarismAcademic Publishing and Open Access

Cited by 4