mlsanityData to 14 Oct 2026

Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation

arXiv 2307.02839 ↗First posted 6 Jul 2023cs.CL cs.AIcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 6 Jul 2023.

Builds on

11 of 35 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  6. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  7. 2017Convolutional Sequence to Sequence Learningcited by 552
  8. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
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  10. 2021Pretrained Language Models for Text Generation: A Surveycited by 95
  11. 2023The Life Cycle of Knowledge in Big Language Models: A Surveycited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 2; 2026: 1.

10 papers.

  1. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  2. 2024Evolutionary Computation in the Era of Large Language Model: Survey and Roadmapcited by 90
  3. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  4. 2023Xpert: Empowering Incident Management with Query Recommendations via Large Language Modelscited by 22
  5. 2024When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challengescited by 20
  6. 2025Cognitive Alpha Mining via LLM-Driven Code-Based Evolutioncited by 15
  7. 2024Mathify: Evaluating Large Language Models on Mathematical Problem Solving Taskscited by 12
  8. 2025A Review on Large Language Models for Visual Analyticscited by 1
  9. 2024News Reporter: A Multi-lingual LLM Framework for Broadcast T.V Newscited by 0
  10. 2026Method for Aggregating Unstructured Data Using Large Language Modelscited by 0