Summary of Xinyu: An Efficient Llm-based System For Commentary Generation, by Yiquan Wu et al.
Xinyu: An Efficient LLM-based System for Commentary Generation
by Yiquan Wu, Bo Tang, Chenyang Xi, Yu Yu, Pengyu Wang, Yifei Liu, Kun Kuang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Jie Hu, Peng Cheng, Zhonghao Wang, Yi Wang, Yi Luo, Mingchuan Yang
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper introduces Xinyu, an efficient Large Language Model (LLM)-based system designed to assist commentators in generating Chinese commentaries. The proposed system addresses two levels of requirements: fundamental and advanced. For fundamental requirements, the generation process is deconstructed into sequential steps with targeted strategies and supervised fine-tuning (SFT) for each step. To meet advanced requirements, an argument ranking model is presented for arguments, and a comprehensive evidence database is established, incorporating up-to-date events and classic books, using retrieval augmented generation (RAG) technology. A comprehensive evaluation metric is introduced to assess generated commentaries from five distinct perspectives. The proposed system demonstrates effectiveness in experiments, significantly increasing efficiency without compromising quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines create commentary articles by simplifying the process. The current way of creating commentary takes a lot of time and effort, even for people who are good at it. Researchers have been using big language models to help with this task, but they need to adapt these models to fit the unique needs of creating commentary. This paper presents a new system called Xinyu that uses large language models to generate Chinese commentary articles more efficiently. The team broke down the process into smaller steps and came up with strategies for each step to make it work better. They also created a database of arguments and evidence to support these commentaries, which helps them sound more convincing. To test this system, they evaluated its performance using five different criteria. The results show that Xinyu works well and makes the process much faster without sacrificing quality. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Rag » Retrieval augmented generation » Supervised
