Madotto, Andrea

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys

Topic ModelingNatural Language Processing Techniques

Cited by 5,025

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Chen, D., Dai, W., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4,981

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

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 508

Personalizing Dialogue Agents via Meta-Learning

Madotto, A., Lin, Z., Wu, C.-S., & Fung, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 183

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Xu, P., Madotto, A., Wu, C.-S., Park, J. H., & Fung, P. (2018) · Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 83

Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 81

Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems

Madotto, A., Liu, Z., Lin, Z., & Fung, P. (2020) · arXiv

Speech and dialogue systemsDomain Adaptation and Few-Shot Learning

Cited by 36

Embodied AI Agents: Modeling the World

Fung, P., Bachrach, Y., Celikyilmaz, A., Chaudhuri, K., Chen, D., Chung, W., Dupoux, E., Gong, H., Jégou, H., Lazaric, A., et al. (2025) · arXiv

Social Robot Interaction and HRIAI-based Problem Solving and Planning

Cited by 29

Perception Encoder: The best visual embeddings are not at the output of the network

Bolya, D., Huang, P.-Y., Sun, P., Cho, J. H., Madotto, A., Wei, C., Ma, T., Zhi, J., Rajasegaran, J., Rasheed, H., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 19

IMU2CLIP: Language-grounded Motion Sensor Translation with Multimodal Contrastive Learning

Moon, S., Madotto, A., Lin, Z., Saraf, A., Bearman, A., & Damavandi, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Multimodal Machine Learning ApplicationsContext-Aware Activity Recognition Systems

Cited by 18

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

Xu, Y., Ishii, E., Cahyawijaya, S., Liu, Z., Winata, G. I., Madotto, A., Su, D., & Fung, P. (2021) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 17

IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text

Moon, S., Madotto, A., Lin, Z., Dirafzoon, A., Saraf, A., Bearman, A., & Damavandi, B. (2022) · arXiv

Multimodal Machine Learning ApplicationsContext-Aware Activity Recognition Systems

Cited by 10

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Cho, J. H., Madotto, A., Mavroudi, E., Afouras, T., Nagarajan, T., Maaz, M., Song, Y., Ma, T., Hu, S., Jain, S., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 8

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Moon, S., Madotto, A., Lin, Z., Nagarajan, T., Smith, M., Jain, S., Yeh, C.-F., Murugesan, P., Heidari, P., Liu, Y., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 7

VScript: Controllable Script Generation with Visual Presentation

Ji, Z., Xu, Y., Cheng, I.-T., Cahyawijaya, S., Frieske, R., Ishii, E., Zeng, M., Madotto, A., & Fung, P. (2022) · arXiv

Artificial Intelligence in GamesSpeech and dialogue systems

Cited by 2