Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Xinyu: An Efficient LLM-based System for Commentary Generation

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

TL;DR

Xinyu is introduced, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries that demonstrates a significant increase in the efficiency of commentators in real-world scenarios, and introduces a comprehensive evaluation metric that considers five distinct perspectives in commentary generation.

Abstract

Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries.

Xinyu: An Efficient LLM-based System for Commentary Generation

TL;DR

Xinyu is introduced, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries that demonstrates a significant increase in the efficiency of commentators in real-world scenarios, and introduces a comprehensive evaluation metric that considers five distinct perspectives in commentary generation.

Abstract

Commentary provides readers with a deep understanding of events by presenting diverse arguments and evidence. However, creating commentary is a time-consuming task, even for skilled commentators. Large language models (LLMs) have simplified the process of natural language generation, but their direct application in commentary creation still faces challenges due to unique task requirements. These requirements can be categorized into two levels: 1) fundamental requirements, which include creating well-structured and logically consistent narratives, and 2) advanced requirements, which involve generating quality arguments and providing convincing evidence. In this paper, we introduce Xinyu, an efficient LLM-based system designed to assist commentators in generating Chinese commentaries. To meet the fundamental requirements, we deconstruct the generation process into sequential steps, proposing targeted strategies and supervised fine-tuning (SFT) for each step. To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology. To evaluate the generated commentaries more fairly, corresponding to the two-level requirements, we introduce a comprehensive evaluation metric that considers five distinct perspectives in commentary generation. Our experiments confirm the effectiveness of our proposed system. We also observe a significant increase in the efficiency of commentators in real-world scenarios, with the average time spent on creating a commentary dropping from 4 hours to 20 minutes. Importantly, such an increase in efficiency does not compromise the quality of the commentaries.
Paper Structure (31 sections, 1 equation, 11 figures, 5 tables)

This paper contains 31 sections, 1 equation, 11 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Large Language Models
  4. Domain-specific LLMs
  5. Preliminaries
  6. Technical route of Xinyu
  7. Main Components
  8. Peg Generation
  9. Main Argument Generation
  10. Supporting Argument Generation
  11. Evidence Generation
  12. Article Combination
  13. Auxiliary Components
  14. Argument Ranking Model
  15. Evidence Database Construction
  16. ...and 16 more sections

Figures (11)

  • Figure 1: The illustration of the commentary generation task. To generate a commentary, it usually requires argument mining, evidence searching, and article embellishing. With the Xinyu, the intermediate steps can be sped up. The right part demonstrates the structure of a commentary, which consists of a title, a main argument, several supporting arguments and evidence, and an ending. This example is translated from Chinese.
  • Figure 2: The overall framework of Xinyu. The generation process is divided into 5 steps.
  • Figure 3: The distribution of SFT datast. Argu. refers to Argument, Sup. means Supporting.
  • Figure 4: Case study. The content is translated from Chinese.
  • Figure 5: Human vs. Xinyu-Assisted.
  • ...and 6 more figures