Zhong, W., Xie, Y., & Lin, J. (2023). Answer Retrieval for Math Questions Using Structural and Dense Retrieval Presented at the Retrieval for Math Questions Using Structural and Dense Retrieval Primary Tabs View conference. https://doi.org/10.1007/978-3-031-42448-9_18
References
Filter by:
2023
Ozsu, T. (2023). Data Science: A Systematic Treatment ArXiv, abs/2301.13761. https://doi.org/10.48550/arXiv.2301.13761
Ozsu, T., & Xue, X. (2023). Preface SDA Presented at the Conference Paper Preface SDA conference. Retrieved from https://ceur-ws.org/Vol-3462/SDA0.pdf
Chen, H., Lassance, C., & Lin, J. (2023). End-to-End Retrieval With Learned Dense and Sparse Representations Using Lucene ArXiv, abs/2311.18503. https://doi.org/10.48550/ARXIV.2311.18503
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
Pradeep, R., Sharifymoghaddam, S., & Lin, J. (2023). RankVicuna: Zero-Shot Listwise Document Reranking With Open-Source Large Language Models ArXiv, abs/2309.15088. https://doi.org/10.48550/ARXIV.2309.15088
Adeyemi, M., Oladipo, A., Pradeep, R., & Lin, J. (2023). Zero-Shot Cross-Lingual Reranking With Large Language Models for Low-Resource Languages ArXiv, abs/2312.16159. https://doi.org/10.48550/ARXIV.2312.16159
Kamalloo, E., Jafari, A., Zhang, X., Thakur, N., & Lin, J. (2023). HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking With Attribution ArXiv, abs/2307.16883. https://doi.org/10.48550/arXiv.2307.16883
Lin, S.-C., Ahmad, A., & Lin, J. (2023). mAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval Presented at the MAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval conference. Retrieved from https://aclanthology.org/2023.emnlp-main.715
Tang, R., Zhang, X., Lin, J., & Türe, F. (2023). What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations ArXiv, abs/2311.18812. https://doi.org/10.48550/ARXIV.2311.18812