Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation
This addresses the problem of generating reliable news summaries for intelligence analysis, though it appears incremental as it combines existing methods like LLM and genetic algorithms.
The paper tackled news summary generation by proposing a new paradigm that uses LLM with evolutionary fine-tuning, resulting in a system that generates accurate and reliable summaries with some generalization ability.
News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.