Lin, J., Li, J., Gao, J., Ma, W., & Liu, Y. (2024). Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification Presented at the Modeling Spatio-Temporal Features of Tactile Signals for Action Classification conference. https://doi.org/10.1609/AAAI.V38I12.29288
References
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2024
Pradeep, R., & Lin, J. (2024). Towards Automated End-to-End Health Misinformation Free Search With A Large Language Model Presented at the Automated End-to-End Health Misinformation Free Search With A Large Language Model conference. https://doi.org/10.1007/978-3-031-56066-8_9
Arabzadeh, N., Bigdeli, A., & Clarke, C. (2024). Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers ArXiv, abs/2401.04842. https://doi.org/10.48550/ARXIV.2401.04842
Shehata, D., Cohen, R., & Clarke, C. (2024). Rumour Evaluation With Very Large Language Models ArXiv, abs/2404.16859. https://doi.org/10.48550/ARXIV.2404.16859
2023
Tamber, M. S., Pradeep, R., & Lin, J. (2023). Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking With Seq2seq Encoder-Decoder Models ArXiv, abs/2312.16098. https://doi.org/10.48550/ARXIV.2312.16098
Huang, C., Xie, Y., Jiang, Z., Lin, J., & Li, M. (2023). Approximating Human-Like Few-Shot Learning With GPT-based Compression ArXiv, abs/2308.06942. https://doi.org/10.48550/arXiv.2308.06942
Lin, S.-C., Asai, A., Li, M., Oguz, B., Lin, J., Mehdad, Y., … Chen, X. (2023). How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval ArXiv, abs/2302.07452. https://doi.org/10.48550/arXiv.2302.07452
Usta, A., & Salihoglu, S. (2023). To Join or Not to Join: An Analysis on the Usefulness of Joining Tables In Open Government Data Portals Presented at the To Join or Not to Join: An Analysis on the Usefulness of Joining Tables In Open Government Data Portals conference. Retrieved from https://ceur-ws.org/Vol-3462/TADA5.pdf
Faggioli, G., Dietz, L., Clarke, C., Demartini, G., Hagen, M., Hauff, C., … Wachsmuth, H. (2023). Perspectives on Large Language Models for Relevance Judgment ArXiv, abs/2304.09161. https://doi.org/10.48550/arXiv.2304.09161
Ma, X., Fun, H., Yin, X., Mallia, A., & Lin, J. (2023). Enhancing Sparse Retrieval via Unsupervised Learning Presented at the Enhancing Sparse Retrieval via Unsupervised Learning conference. https://doi.org/10.1145/3624918.3625334