ARCHIVES

1. Arghya Mukherjee. CommerceCore: prior query-parsing model and research artifacts. Public project manuscript, 2026.
https://github.com/arghya05/commercecore/tree/main/ paper.
2. Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, and Xia Ning. eCeLLM: Generalizing Large Language Models for E-commerce from Largescale,
High-quality Instruction Data. In ICML, 2024. https://arxiv.org/abs/2402.08831.
3. Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, and Yong Jiang.
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce. 2023. https://arxiv.org/abs/2308. 06966.
4. Xinyi Ling, Hanwen Du, Bo Peng, Zhihui Zhu, and Xia Ning. Captions Speak Louder than Images: Generalizing Foundation Models for Ecommerce
from High-quality Multimodal In-struction Data. In IJCNLP-AACL, 2025. https://arxiv.org/abs/2410.17337.
5. Josip Tomo Licardo and Nikola Tankovic´. Performance Trade-offs of Optimizing Small Lan-guage Models for E-Commerce. 2025.
https://arxiv.org/abs/2510.21970.
6. Chandan K. Reddy, Lluís Màrquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing,
and Karthik Subbian. Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search. 2022. https://arxiv.org/
abs/2206.06588.
7. Ralph Peeters, Reng Chiz Der, and Christian Bizer. WDC Products: A Multi-Dimensional Entity Matching Benchmark. In EDBT, 2024.
https://arxiv.org/abs/2301.09521.
8. Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. Deep En-tity Matching with Pre-Trained Language
Models. Proceedings of the VLDB Endowment, 14(1):50–60, 2020. https://arxiv.org/abs/2004.00584.
9. Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, and Ash Lewis. GLiNER2: An Efficient Multi-Task Information
Extraction System with Schema-Driven In-terface. 2025. https://arxiv.org/abs/2507.18546.
10. Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F. Yago Vicente, Thomas
Dideriksen, Himanshu Arora, Matthieu Guillaumin, and Jitendra Malik. ABO: Dataset and Benchmarks for Real-World 3D Object
Understanding. In CVPR, 2022. https://arxiv.org/abs/2110.06199.
11. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-Rank
Adaptation of Large Language Models. In ICLR, 2022. https://arxiv.org/abs/2106.09685.
12. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient Finetuning of Quantized LLMs. In NeurIPS,
2023. https://arxiv.org/abs/2305.14314.
13. Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. GradNorm: Gradi-ent Normalization for Adaptive Loss Balancing
in Deep Multitask Networks. In ICML, 2018. https://arxiv.org/abs/1711.02257.
14. Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient Surgery for Multi-Task
Learning. In NeurIPS, 2020. https://arxiv.org/ abs/2001.06782.
15. Sayash Kapoor and Arvind Narayanan. Leakage and the Reproducibility Crisis in ML-based Science. 2022.
https://arxiv.org/abs/2207.07048.
16. Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. Beyond Accuracy: Behavioral Testing of NLP Models with
CheckList. In ACL, 2020. https://arxiv.org/ abs/2005.04118.