Summary of Shopping Mmlu: a Massive Multi-task Online Shopping Benchmark For Large Language Models, by Yilun Jin et al.
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
by Yilun Jin, Zheng Li, Chenwei Zhang, Tianyu Cao, Yifan Gao, Pratik Jayarao, Mao Li, Xin Liu, Ritesh Sarkhel, Xianfeng Tang, Haodong Wang, Zhengyang Wang, Wenju Xu, Jingfeng Yang, Qingyu Yin, Xian Li, Priyanka Nigam, Yi Xu, Kai Chen, Qiang Yang, Meng Jiang, Bing Yin
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Shopping MMLU benchmark aims to comprehensively evaluate the abilities of Large Language Models (LLMs) as general shop assistants. It consists of 57 tasks covering four major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality. The benchmark is derived from real-world Amazon data and can alleviate task-specific engineering efforts for LLMs in online shopping. By benchmarking over 20 existing LLMs, the study uncovers valuable insights about practices and prospects of building versatile LLM-based shop assistants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shopping MMLU is a new way to test how well large language models can help with online shopping. It’s like a big quiz that asks the models lots of different questions about things you might find on Amazon, like what something means or how to make sense of some data. The goal is to see if these models can become helpful assistants for shoppers, without needing special training for each specific task. |
Keywords
» Artificial intelligence » Alignment
