# CAP-LLM: Context-Augmented Personalized Large Language Models for News Headline Generation

arXiv: https://arxiv.org/abs/2508.03935 · HTML: https://mlsanity.com/paper/2508.03935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models](https://mlsanity.com/paper/2305.11171/) (2023)
- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://mlsanity.com/paper/2012.00413/) (2020)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback](https://mlsanity.com/paper/2501.01377/) (2025)
- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration](https://mlsanity.com/paper/2506.19835/) (2025)
- [Learning Personalized Alignment for Evaluating Open-ended Text Generation](https://mlsanity.com/paper/2310.03304/) (2023)
- [Personalized Text Generation with Fine-Grained Linguistic Control](https://mlsanity.com/paper/2402.04914/) (2024)
- [Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)](https://mlsanity.com/paper/2407.12858/) (2024)
- [Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling](https://mlsanity.com/paper/2505.15715/) (2025)
- [Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation](https://mlsanity.com/paper/2505.16763/) (2025)
- [COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer](https://mlsanity.com/paper/2110.08427/) (2021)
- [PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting](https://mlsanity.com/paper/2409.17703/) (2024)
- [GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models](https://mlsanity.com/paper/2503.07463/) (2025)
- [Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models](https://mlsanity.com/paper/2503.00449/) (2025)
- [Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary](https://mlsanity.com/paper/1412.5448/) (2014)

## Built on by (most cited first, 1 of 1)

- [AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization](https://mlsanity.com/paper/2609.19793/) (2026)

## Abstract (arXiv)

In the era of information overload, personalized news headline generation is crucial for engaging users by tailoring content to their preferences while accurately conveying news facts. Existing methods struggle with effectively capturing complex user interests and ensuring factual consistency, often leading to generic or misleading headlines. Leveraging the unprecedented capabilities of Large Language Models (LLMs) in text generation, we propose Context-Augmented Personalized LLM (CAP-LLM), a novel framework that integrates user preferences and factual consistency constraints into a powerful pre-trained LLM backbone. CAP-LLM features a User Preference Encoder to capture long-term user interests, a Context Injection Adapter to seamlessly integrate these preferences and current article context into the LLM's generation process, and a Fact-Consistency Reinforcement Module employing a novel contrastive loss to mitigate hallucination. Evaluated on the real-world PENS dataset, CAP-LLM achieves state-of-the-art performance across all metrics. Notably, it significantly improves factual consistency (FactCC of 87.50) over strong baselines like BART (86.67), while simultaneously enhancing personalization (Pc(avg) 2.73, Pc(max) 17.25) and content coverage (ROUGE-1 26.55, ROUGE-2 9.95, ROUGE-L 23.01). Our ablation studies, human evaluations, and sensitivity analyses further validate the effectiveness of each component and the robustness of our approach, demonstrating CAP-LLM's ability to achieve a superior balance between personalization and factual accuracy in news headline generation.